WorldWideScience

Sample records for regression discontinuity estimates

  1. Using a Regression Discontinuity Design to Estimate the Impact of Placement Decisions in Developmental Math

    Science.gov (United States)

    Melguizo, Tatiana; Bos, Johannes M.; Ngo, Federick; Mills, Nicholas; Prather, George

    2016-01-01

    This study evaluates the effectiveness of math placement policies for entering community college students on these students' academic success in math. We estimate the impact of placement decisions by using a discrete-time survival model within a regression discontinuity framework. The primary conclusion that emerges is that initial placement in a…

  2. Replicating Experimental Impact Estimates Using a Regression Discontinuity Approach. NCEE 2012-4025

    Science.gov (United States)

    Gleason, Philip M.; Resch, Alexandra M.; Berk, Jillian A.

    2012-01-01

    This NCEE Technical Methods Paper compares the estimated impacts of an educational intervention using experimental and regression discontinuity (RD) study designs. The analysis used data from two large-scale randomized controlled trials--the Education Technology Evaluation and the Teach for America Study--to provide evidence on the performance of…

  3. Engineering estimates versus impact evaluation of energy efficiency projects: Regression discontinuity evidence from a case study

    International Nuclear Information System (INIS)

    Lang, Corey; Siler, Matthew

    2013-01-01

    Energy efficiency upgrades have been gaining widespread attention across global channels as a cost-effective approach to addressing energy challenges. The cost-effectiveness of these projects is generally predicted using engineering estimates pre-implementation, often with little ex post analysis of project success. In this paper, for a suite of energy efficiency projects, we directly compare ex ante engineering estimates of energy savings to ex post econometric estimates that use 15-min interval, building-level energy consumption data. In contrast to most prior literature, our econometric results confirm the engineering estimates, even suggesting the engineering estimates were too modest. Further, we find heterogeneous efficiency impacts by time of day, suggesting select efficiency projects can be useful in reducing peak load. - Highlights: • Regression discontinuity used to estimate energy savings from efficiency projects. • Ex post econometric estimates validate ex ante engineering estimates of energy savings. • Select efficiency projects shown to reduce peak load

  4. Regression Discontinuity and Randomized Controlled Trial Estimates: An Application to The Mycotic Ulcer Treatment Trials.

    Science.gov (United States)

    Oldenburg, Catherine E; Venkatesh Prajna, N; Krishnan, Tiruvengada; Rajaraman, Revathi; Srinivasan, Muthiah; Ray, Kathryn J; O'Brien, Kieran S; Glymour, M Maria; Porco, Travis C; Acharya, Nisha R; Rose-Nussbaumer, Jennifer; Lietman, Thomas M

    2018-08-01

    We compare results from regression discontinuity (RD) analysis to primary results of a randomized controlled trial (RCT) utilizing data from two contemporaneous RCTs for treatment of fungal corneal ulcers. Patients were enrolled in the Mycotic Ulcer Treatment Trials I and II (MUTT I & MUTT II) based on baseline visual acuity: patients with acuity ≤ 20/400 (logMAR 1.3) enrolled in MUTT I, and >20/400 in MUTT II. MUTT I investigated the effect of topical natamycin versus voriconazole on best spectacle-corrected visual acuity. MUTT II investigated the effect of topical voriconazole plus placebo versus topical voriconazole plus oral voriconazole. We compared the RD estimate (natamycin arm of MUTT I [N = 162] versus placebo arm of MUTT II [N = 54]) to the RCT estimate from MUTT I (topical natamycin [N = 162] versus topical voriconazole [N = 161]). In the RD, patients receiving natamycin had mean improvement of 4-lines of visual acuity at 3 months (logMAR -0.39, 95% CI: -0.61, -0.17) compared to topical voriconazole plus placebo, and 2-lines in the RCT (logMAR -0.18, 95% CI: -0.30, -0.05) compared to topical voriconazole. The RD and RCT estimates were similar, although the RD design overestimated effects compared to the RCT.

  5. Testing discontinuities in nonparametric regression

    KAUST Repository

    Dai, Wenlin

    2017-01-19

    In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

  6. Testing discontinuities in nonparametric regression

    KAUST Repository

    Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun

    2017-01-01

    In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

  7. Estimating Unbiased Treatment Effects in Education Using a Regression Discontinuity Design

    Directory of Open Access Journals (Sweden)

    William C. Smith

    2014-08-01

    Full Text Available The ability of regression discontinuity (RD designs to provide an unbiased treatment effect while overcoming the ethical concerns plagued by Random Control Trials (RCTs make it a valuable and useful approach in education evaluation. RD is the only explicitly recognized quasi-experimental approach identified by the Institute of Education Statistics to meet the prerequisites of a causal relationship. Unfortunately, the statistical complexity of the RD design has limited its application in education research. This article provides a less technical introduction to RD for education researchers and practitioners. Using visual analysis to aide conceptual understanding, the article walks readers through the essential steps of a Sharp RD design using hypothetical, but realistic, district intervention data and provides additional resources for further exploration.

  8. Estimating the real-world effects of expanding antiretroviral treatment eligibility: Evidence from a regression discontinuity analysis in Zambia.

    Directory of Open Access Journals (Sweden)

    Aaloke Mody

    2018-06-01

    Full Text Available Although randomized trials have established the clinical efficacy of treating all persons living with HIV (PLWHs, expanding treatment eligibility in the real world may have additional behavioral effects (e.g., changes in retention or lead to unintended consequences (e.g., crowding out sicker patients owing to increased patient volume. Using a regression discontinuity design, we sought to assess the effects of a previous change to Zambia's HIV treatment guidelines increasing the threshold for treatment eligibility from 350 to 500 cells/μL to anticipate effects of current global efforts to treat all PLWHs.We analyzed antiretroviral therapy (ART-naïve adults who newly enrolled in HIV care in a network of 64 clinics operated by the Zambian Ministry of Health and supported by the Centre for Infectious Disease Research in Zambia (CIDRZ. Patients were restricted to those enrolling in a narrow window around the April 1, 2014 change to Zambian HIV treatment guidelines that raised the CD4 threshold for treatment from 350 to 500 cells/μL (i.e., August 1, 2013, to November 1, 2014. Clinical and sociodemographic data were obtained from an electronic medical record system used in routine care. We used a regression discontinuity design to estimate the effects of this change in treatment eligibility on ART initiation within 3 months of enrollment, retention in care at 6 months (defined as clinic attendance between 3 and 9 months after enrollment, and a composite of both ART initiation by 3 months and retention in care at 6 months in all new enrollees. We also performed an instrumental variable (IV analysis to quantify the effect of actually initiating ART because of this guideline change on retention. Overall, 34,857 ART-naïve patients (39.1% male, median age 34 years [IQR 28-41], median CD4 268 cells/μL [IQR 134-430] newly enrolled in HIV care during this period; 23,036 were analyzed after excluding patients around the threshold to allow for clinic

  9. Regression Discontinuity Designs Based on Population Thresholds

    DEFF Research Database (Denmark)

    Eggers, Andrew C.; Freier, Ronny; Grembi, Veronica

    In many countries, important features of municipal government (such as the electoral system, mayors' salaries, and the number of councillors) depend on whether the municipality is above or below arbitrary population thresholds. Several papers have used a regression discontinuity design (RDD...

  10. A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs

    Science.gov (United States)

    Karabatsos, George; Walker, Stephen G.

    2013-01-01

    The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…

  11. Grades, Gender, and Encouragement: A Regression Discontinuity Analysis

    Science.gov (United States)

    Owen, Ann L.

    2010-01-01

    The author employs a regression discontinuity design to provide direct evidence on the effects of grades earned in economics principles classes on the decision to major in economics and finds a differential effect for male and female students. Specifically, for female students, receiving an A for a final grade in the first economics class is…

  12. How Can Comparison Groups Strengthen Regression Discontinuity Designs?

    Science.gov (United States)

    Wing, Coady; Cook, Thomas D.

    2011-01-01

    In this paper, the authors examine some of the ways that different types of non-equivalent comparison groups can be used to strengthen causal inferences based on regression discontinuity design (RDD). First, they consider a design that incorporates pre-test data on assignment scores and outcomes that were collected either before the treatment…

  13. The price sensitivity of Medicare beneficiaries: a regression discontinuity approach.

    Science.gov (United States)

    Buchmueller, Thomas C; Grazier, Kyle; Hirth, Richard A; Okeke, Edward N

    2013-01-01

    We use 4 years of data from the retiree health benefits program of the University of Michigan to estimate the effect of price on the health plan choices of Medicare beneficiaries. During the period of our analysis, changes in the University's premium contribution rules led to substantial price changes. A key feature of this 'natural experiment' is that individuals who had retired before a certain date were exempted from having to pay any premium contributions. This 'grandfathering' creates quasi-experimental variation that is ideal for estimating the effect of price. Using regression discontinuity methods, we compare the plan choices of individuals who retired just after the grandfathering cutoff date and were therefore exposed to significant price changes to the choices of a 'control group' of individuals who retired just before that date and therefore did not experience the price changes. The results indicate a statistically significant effect of price, with a $10 increase in monthly premium contributions leading to a 2 to 3 percentage point decrease in a plan's market share. Copyright © 2012 John Wiley & Sons, Ltd.

  14. Strengthening the Regression Discontinuity Design Using Additional Design Elements: A Within-Study Comparison

    Science.gov (United States)

    Wing, Coady; Cook, Thomas D.

    2013-01-01

    The sharp regression discontinuity design (RDD) has three key weaknesses compared to the randomized clinical trial (RCT). It has lower statistical power, it is more dependent on statistical modeling assumptions, and its treatment effect estimates are limited to the narrow subpopulation of cases immediately around the cutoff, which is rarely of…

  15. Health Care Facility Choice and User Fee Abolition: Regression Discontinuity in a Multinomial Choice Setting

    OpenAIRE

    Steven F. Koch; Jeffrey S. Racine

    2013-01-01

    We apply parametric and nonparametric regression discontinuity methodology within a multinomial choice setting to examine the impact of public health care user fee abolition on health facility choice using data from South Africa. The nonparametric model is found to outperform the parametric model both in- and out-of-sample, while also delivering more plausible estimates of the impact of user fee abolition (i.e. the 'treatment effect'). In the parametric framework, treatment effects were relat...

  16. Regression Discontinuity in Prospective Evaluations: The Case of the FFVP Evaluation

    Science.gov (United States)

    Klerman, Jacob Alex; Olsho, Lauren E. W.; Bartlett, Susan

    2015-01-01

    While regression discontinuity has usually been applied retrospectively to secondary data, it is even more attractive when applied prospectively. In a prospective design, data collection can be focused on cases near the discontinuity, thereby improving internal validity and substantially increasing precision. Furthermore, such prospective…

  17. Evaluating effects of developmental education for college students using a regression discontinuity design.

    Science.gov (United States)

    Moss, Brian G; Yeaton, William H

    2013-10-01

    Annually, American colleges and universities provide developmental education (DE) to millions of underprepared students; however, evaluation estimates of DE benefits have been mixed. Using a prototypic exemplar of DE, our primary objective was to investigate the utility of a replicative evaluative framework for assessing program effectiveness. Within the context of the regression discontinuity (RD) design, this research examined the effectiveness of a DE program for five, sequential cohorts of first-time college students. Discontinuity estimates were generated for individual terms and cumulatively, across terms. Participants were 3,589 first-time community college students. DE program effects were measured by contrasting both college-level English grades and a dichotomous measure of pass/fail, for DE and non-DE students. Parametric and nonparametric estimates of overall effect were positive for continuous and dichotomous measures of achievement (grade and pass/fail). The variability of program effects over time was determined by tracking results within individual terms and cumulatively, across terms. Applying this replication strategy, DE's overall impact was modest (an effect size of approximately .20) but quite consistent, based on parametric and nonparametric estimation approaches. A meta-analysis of five RD results yielded virtually the same estimate as the overall, parametric findings. Subset analysis, though tentative, suggested that males benefited more than females, while academic gains were comparable for different ethnicities. The cumulative, within-study comparison, replication approach offers considerable potential for the evaluation of new and existing policies, particularly when effects are relatively small, as is often the case in applied settings.

  18. Financial Aid and First-Year Collegiate GPA: A Regression Discontinuity Approach

    Science.gov (United States)

    Curs, Bradley R.; Harper, Casandra E.

    2012-01-01

    Using a regression discontinuity design, we investigate whether a merit-based financial aid program has a causal effect on the first-year grade point average of first-time out-of-state freshmen at the University of Oregon. Our results indicate that merit-based financial aid has a positive and significant effect on first-year collegiate grade point…

  19. Quantitative Estimation of Transmitted and Reflected Lamb Waves at Discontinuity

    International Nuclear Information System (INIS)

    Lim, Hyung Jin; Sohn, Hoon

    2010-01-01

    For the application of Lamb wave to structural health monitoring(SHM), understanding its physical characteristic and interaction between Lamb wave and defect of the host structure is an important issue. In this study, reflected, transmitted and mode converted Lamb waves at discontinuity of a plate structure were simulated and the amplitude ratios are calculated theoretically using Modal decomposition method. The predicted results were verified comparing with finite element method(FEM) and experimental results simulating attached PZTs. The result shows that the theoretical prediction is close to the FEM and the experimental verification. Moreover, quantitative estimation method was suggested using amplitude ratio of Lamb wave at discontinuity

  20. Skipping Class in College and Exam Performance: Evidence from a Regression Discontinuity Classroom Experiment

    Science.gov (United States)

    Dobkin, Carlos; Gil, Ricard; Marion, Justin

    2010-01-01

    In this paper we estimate the effect of class attendance on exam performance by implementing a policy in three large economics classes that required students scoring below the median on the midterm exam to attend class. This policy generated a large discontinuity in the rate of post-midterm attendance at the median of the midterm score. We…

  1. Dynamic travel time estimation using regression trees.

    Science.gov (United States)

    2008-10-01

    This report presents a methodology for travel time estimation by using regression trees. The dissemination of travel time information has become crucial for effective traffic management, especially under congested road conditions. In the absence of c...

  2. Institutions and deforestation in the Brazilian amazon: a geographic regression discontinuity analysis

    OpenAIRE

    Bogetvedt, Ingvild Engen; Hauge, Mari Johnsrud

    2017-01-01

    This study explores the impact of institutional quality at the municipal level on deforestation in the Legal Amazon. We add to this insufficiently understood topic by implementing a geographic regression discontinuity design. By taking advantage of high-resolution spatial data on deforestation combined with an objective measure of corruption used as a proxy for institutional quality, we analyse 138 Brazilian municipalities in the period of 2002-2004. Our empirical findings show...

  3. Estimation of the Continuous and Discontinuous Leverage Effects.

    Science.gov (United States)

    Aït-Sahalia, Yacine; Fan, Jianqing; Laeven, Roger J A; Wang, Christina Dan; Yang, Xiye

    2017-01-01

    This paper examines the leverage effect, or the generally negative covariation between asset returns and their changes in volatility, under a general setup that allows the log-price and volatility processes to be Itô semimartingales. We decompose the leverage effect into continuous and discontinuous parts and develop statistical methods to estimate them. We establish the asymptotic properties of these estimators. We also extend our methods and results (for the continuous leverage) to the situation where there is market microstructure noise in the observed returns. We show in Monte Carlo simulations that our estimators have good finite sample performance. When applying our methods to real data, our empirical results provide convincing evidence of the presence of the two leverage effects, especially the discontinuous one.

  4. Regression Equations for Birth Weight Estimation using ...

    African Journals Online (AJOL)

    In this study, Birth Weight has been estimated from anthropometric measurements of hand and foot. Linear regression equations were formed from each of the measured variables. These simple equations can be used to estimate Birth Weight of new born babies, in order to identify those with low birth weight and referred to ...

  5. Principal component regression for crop yield estimation

    CERN Document Server

    Suryanarayana, T M V

    2016-01-01

    This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...

  6. Robust median estimator in logisitc regression

    Czech Academy of Sciences Publication Activity Database

    Hobza, T.; Pardo, L.; Vajda, Igor

    2008-01-01

    Roč. 138, č. 12 (2008), s. 3822-3840 ISSN 0378-3758 R&D Projects: GA MŠk 1M0572 Grant - others:Instituto Nacional de Estadistica (ES) MPO FI - IM3/136; GA MŠk(CZ) MTM 2006-06872 Institutional research plan: CEZ:AV0Z10750506 Keywords : Logistic regression * Median * Robustness * Consistency and asymptotic normality * Morgenthaler * Bianco and Yohai * Croux and Hasellbroeck Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.679, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/vajda-robust%20median%20estimator%20in%20logistic%20regression.pdf

  7. Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa.

    Directory of Open Access Journals (Sweden)

    Jacob Bor

    2017-11-01

    Full Text Available Loss to follow-up is high among HIV patients not yet receiving antiretroviral therapy (ART. Clinical trials have demonstrated the clinical efficacy of early ART; however, these trials may miss an important real-world consequence of providing ART at diagnosis: its impact on retention in care.We examined the effect of immediate (versus deferred ART on retention in care using a regression discontinuity design. The analysis included all patients (N = 11,306 entering clinical HIV care with a first CD4 count between 12 August 2011 and 31 December 2012 in a public-sector HIV care and treatment program in rural South Africa. Patients were assigned to immediate versus deferred ART eligibility, as determined by a CD4 count < 350 cells/μl, per South African national guidelines. Patients referred to pre-ART care were instructed to return every 6 months for CD4 monitoring. Patients initiated on ART were instructed to return at 6 and 12 months post-initiation and annually thereafter for CD4 and viral load monitoring. We assessed retention in HIV care at 12 months, as measured by the presence of a clinic visit, lab test, or ART initiation 6 to 18 months after initial CD4 test. Differences in retention between patients presenting with CD4 counts just above versus just below the 350-cells/μl threshold were estimated using local linear regression models with a data-driven bandwidth and with the algorithm for selecting the bandwidth chosen ex ante. Among patients with CD4 counts close to the 350-cells/μl threshold, having an ART-eligible CD4 count (<350 cells/μl was associated with higher 12-month retention than not having an ART-eligible CD4 count (50% versus 32%, an intention-to-treat risk difference of 18 percentage points (95% CI 11 to 23; p < 0.001. The decision to start ART was determined by CD4 count for one in four patients (25% presenting close to the eligibility threshold (95% CI 20% to 31%; p < 0.001. In this subpopulation, having an ART-eligible CD

  8. Effect of air quality alerts on human health: a regression discontinuity analysis in Toronto, Canada.

    Science.gov (United States)

    Chen, Hong; Li, Qiongsi; Kaufman, Jay S; Wang, Jun; Copes, Ray; Su, Yushan; Benmarhnia, Tarik

    2018-01-01

    Ambient air pollution is a major health risk globally. To reduce adverse health effects on days when air pollution is high, government agencies worldwide have implemented air quality alert programmes. Despite their widespread use, little is known about whether these programmes produce any observable public-health benefits. We assessed the effectiveness of such programmes using a quasi-experimental approach. We assembled a population-based cohort comprising all individuals who resided in the city of Toronto (Ontario, Canada) from 2003 to 2012 (about 2·6 million people). We ascertained seven health outcomes known to be affected by short-term elevation of air pollution, using provincial health administrative databases. These health outcomes were cardiovascular-related mortality, respiratory-related mortality, and hospital admissions or emergency-department visits for acute myocardial infarction, heart failure, stroke, asthma, and chronic obstructive pulmonary disease (COPD). We applied a regression discontinuity design to assess the effectiveness of an intervention (ie, the air quality alert programme). To quantify the effect of the air quality alert programme, we estimated for each outcome both the absolute rate difference and the rate ratio attributable to programme eligibility (by intention-to-treat analysis) and the alerts themselves (by two-stage regression approach), respectively. Between Jan 1, 2003, and Dec 31, 2012, on average between three and 27 daily cardiovascular or respiratory events were reported in Toronto (depending on the outcome). Alert announcements reduced asthma-related emergency-department visits by 4·73 cases per 1 000 000 people per day (95% CI 0·55-9·38), or in relative terms by 25% (95% CI 1-47). Programme eligibility also led to 2·05 (95% CI 0·07-4·00) fewer daily emergency-department visits for asthma. We did not detect a significant reduction in any other health outcome as a result of alert announcements or programme

  9. Evaluating disease management programme effectiveness: an introduction to the regression discontinuity design.

    Science.gov (United States)

    Linden, Ariel; Adams, John L; Roberts, Nancy

    2006-04-01

    Although disease management (DM) has been in existence for over a decade, there is still much uncertainty as to its effectiveness in improving health status and reducing medical cost. The main reason is that most programme evaluations typically follow weak observational study designs that are subject to bias, most notably selection bias and regression to the mean. The regression discontinuity (RD) design may be the best alternative to randomized studies for evaluating DM programme effectiveness. The most crucial element of the RD design is its use of a 'cut-off' score on a pre-test measure to determine assignment to intervention or control. A valuable feature of this technique is that the pre-test measure does not have to be the same as the outcome measure, thus maximizing the programme's ability to use research-based practice guidelines, survey instruments and other tools to identify those individuals in greatest need of the programme intervention. Similarly, the cut-off score can be based on clinical understanding of the disease process, empirically derived, or resource-based. In the RD design, programme effectiveness is determined by a change in the pre-post relationship at the cut-off point. While the RD design is uniquely suitable for DM programme evaluation, its success will depend, in large part, on fundamental changes being made in the way DM programmes identify and assign individuals to the programme intervention.

  10. Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation

    Directory of Open Access Journals (Sweden)

    Sharad Damodar Gore

    2009-10-01

    Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.

  11. Relative Age in School and Suicide among Young Individuals in Japan: A Regression Discontinuity Approach.

    Directory of Open Access Journals (Sweden)

    Tetsuya Matsubayashi

    Full Text Available Evidence collected in many parts of the world suggests that, compared to older students, students who are relatively younger at school entry tend to have worse academic performance and lower levels of income. This study examined how relative age in a grade affects suicide rates of adolescents and young adults between 15 and 25 years of age using data from Japan.We examined individual death records in the Vital Statistics of Japan from 1989 to 2010. In contrast to other countries, late entry to primary school is not allowed in Japan. We took advantage of the school entry cutoff date to implement a regression discontinuity (RD design, assuming that the timing of births around the school entry cutoff date was randomly determined and therefore that individuals who were born just before and after the cutoff date have similar baseline characteristics.We found that those who were born right before the school cutoff day and thus youngest in their cohort have higher mortality rates by suicide, compared to their peers who were born right after the cutoff date and thus older. We also found that those with relative age disadvantage tend to follow a different career path than those with relative age advantage, which may explain their higher suicide mortality rates.Relative age effects have broader consequences than was previously supposed. This study suggests that policy intervention that alleviates the relative age effect can be important.

  12. A gradient estimate for solutions to parabolic equations with discontinuous coefficients

    OpenAIRE

    Fan, Jishan; Kim, Kyoungsun; Nagayasu, Sei; Nakamura, Gen

    2011-01-01

    Li-Vogelius and Li-Nirenberg gave a gradient estimate for solutions of strongly elliptic equations and systems of divergence forms with piecewise smooth coefficients, respectively. The discontinuities of the coefficients are assumed to be given by manifolds of codimension 1, which we called them emph{manifolds of discontinuities}. Their gradient estimate is independent of the distances between manifolds of discontinuities. In this paper, we gave a parabolic version of their results. T...

  13. How do urban households in China respond to increasing block pricing in electricity? Evidence from a fuzzy regression discontinuity approach

    International Nuclear Information System (INIS)

    Zhang, Zibin; Cai, Wenxin; Feng, Xiangzhao

    2017-01-01

    China is the largest electricity consumption country after it has passed the United States in 2011. Residential electricity consumption in China grew by 381.35% (12.85% per annum) between 2000 and 2013. In order to deal with rapid growth in residential electricity consumption, an increasing block pricing policy was introduced for residential electricity consumers in China on July 1st, 2012. Using difference-in-differences models with a fuzzy regression discontinuity design, we estimate a causal effect of price on electricity consumption for urban households during the introduction of increasing block pricing policy in Guangdong province of China. We find that consumers do not respond to a smaller (approximately 8%) increase in marginal price. However, consumers do respond to a larger increase in marginal price. An approximately 40% increase in marginal price induces an approximately 35% decrease in electricity use (284 kW h per month). Our results suggest that although the increasing block pricing could affect the behavior of households with higher electricity use, there is only a limit potential to overall energy conservation. - Highlights: • Estimate electricity consumption changes in response to the IBP in China. • Employ quasi-experimental approach and micro household level data in China. • Households do not respond to a smaller increase in marginal price. • 40% increase in marginal price induces a 35% decrease in electricity use.

  14. A gradient estimate for solutions to parabolic equations with discontinuous coefficients

    Directory of Open Access Journals (Sweden)

    Jishan Fan

    2013-04-01

    Full Text Available Li-Vogelius and Li-Nirenberg gave a gradient estimate for solutions of strongly elliptic equations and systems of divergence forms with piecewise smooth coefficients, respectively. The discontinuities of the coefficients are assumed to be given by manifolds of codimension 1, which we called them emph{manifolds of discontinuities}. Their gradient estimate is independent of the distances between manifolds of discontinuities. In this paper, we gave a parabolic version of their results. That is, we gave a gradient estimate for parabolic equations of divergence forms with piecewise smooth coefficients. The coefficients are assumed to be independent of time and their discontinuities are likewise the previous elliptic equations. As an application of this estimate, we also gave a pointwise gradient estimate for the fundamental solution of a parabolic operator with piecewise smooth coefficients. Both gradient estimates are independent of the distances between manifolds of discontinuities.

  15. The impact of medical insurance for the poor in Georgia: a regression discontinuity approach.

    Science.gov (United States)

    Bauhoff, Sebastian; Hotchkiss, David R; Smith, Owen

    2011-11-01

    Improving access to health care and financial protection of the poor is a key concern for policymakers in low- and middle-income countries, but there have been few rigorous program evaluations. The Medical Insurance Program for the Poor in the republic of Georgia provides a free and extensive benefit package and operates through a publicly funded voucher program, enabling beneficiaries to choose their own private insurance company. Eligibility is determined by a proxy means test administered to applicant households. The objective of this study is to evaluate the program's impact on key outcomes including utilization, financial risk protection, and health behavior and management. A dedicated survey of approximately 3500 households around the thresholds was designed to minimize unobserved heterogeneity by sampling clusters with both beneficiary and non-beneficiary households. The research design exploits the sharp discontinuities at two regional eligibility thresholds to estimate local average treatment effects. Results suggest that the program did not affect utilization of health services but decreased mean out-of-pocket expenditures for some groups and reduced the risk of high inpatient expenditures. There are no systematic impacts on health behavior, management of chronic illnesses, and patient satisfaction. Copyright © 2010 John Wiley & Sons, Ltd.

  16. Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa.

    Science.gov (United States)

    Bor, Jacob; Fox, Matthew P; Rosen, Sydney; Venkataramani, Atheendar; Tanser, Frank; Pillay, Deenan; Bärnighausen, Till

    2017-11-01

    Loss to follow-up is high among HIV patients not yet receiving antiretroviral therapy (ART). Clinical trials have demonstrated the clinical efficacy of early ART; however, these trials may miss an important real-world consequence of providing ART at diagnosis: its impact on retention in care. We examined the effect of immediate (versus deferred) ART on retention in care using a regression discontinuity design. The analysis included all patients (N = 11,306) entering clinical HIV care with a first CD4 count between 12 August 2011 and 31 December 2012 in a public-sector HIV care and treatment program in rural South Africa. Patients were assigned to immediate versus deferred ART eligibility, as determined by a CD4 count linear regression models with a data-driven bandwidth and with the algorithm for selecting the bandwidth chosen ex ante. Among patients with CD4 counts close to the 350-cells/μl threshold, having an ART-eligible CD4 count (<350 cells/μl) was associated with higher 12-month retention than not having an ART-eligible CD4 count (50% versus 32%), an intention-to-treat risk difference of 18 percentage points (95% CI 11 to 23; p < 0.001). The decision to start ART was determined by CD4 count for one in four patients (25%) presenting close to the eligibility threshold (95% CI 20% to 31%; p < 0.001). In this subpopulation, having an ART-eligible CD4 count was associated with higher 12-month retention than not having an ART-eligible CD4 count (91% versus 21%), a complier causal risk difference of 70 percentage points (95% CI 42 to 98; p < 0.001). The major limitations of the study are the potential for limited generalizability, the potential for outcome misclassification, and the absence of data on longer-term health outcomes. Patients who were eligible for immediate ART had dramatically higher retention in HIV care than patients who just missed the CD4-count eligibility cutoff. The clinical and population health benefits of offering immediate ART regardless

  17. A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters

    NARCIS (Netherlands)

    Peng, Yijie; Fu, Michael C.; Hu, Jian Qiang; Heidergott, Bernd

    In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2)

  18. Evaluating an Organizational-Level Occupational Health Intervention in a Combined Regression Discontinuity and Randomized Control Design.

    Science.gov (United States)

    Sørensen, By Ole H

    2016-10-01

    Organizational-level occupational health interventions have great potential to improve employees' health and well-being. However, they often compare unfavourably to individual-level interventions. This calls for improving methods for designing, implementing and evaluating organizational interventions. This paper presents and discusses the regression discontinuity design because, like the randomized control trial, it is a strong summative experimental design, but it typically fits organizational-level interventions better. The paper explores advantages and disadvantages of a regression discontinuity design with an embedded randomized control trial. It provides an example from an intervention study focusing on reducing sickness absence in 196 preschools. The paper demonstrates that such a design fits the organizational context, because it allows management to focus on organizations or workgroups with the most salient problems. In addition, organizations may accept an embedded randomized design because the organizations or groups with most salient needs receive obligatory treatment as part of the regression discontinuity design. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  19. A logistic regression estimating function for spatial Gibbs point processes

    DEFF Research Database (Denmark)

    Baddeley, Adrian; Coeurjolly, Jean-François; Rubak, Ege

    We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related to the p......We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related...

  20. Block Volume Estimation from the Discontinuity Spacing Measurements of Mesozoic Limestone Quarries, Karaburun Peninsula, Turkey

    OpenAIRE

    Elci, Hakan; Turk, Necdet

    2014-01-01

    Block volumes are generally estimated by analyzing the discontinuity spacing measurements obtained either from the scan lines placed over the rock exposures or the borehole cores. Discontinuity spacing measurements made at the Mesozoic limestone quarries in Karaburun Peninsula were used to estimate the average block volumes that could be produced from them using the suggested methods in the literature. The Block Quality Designation (BQD) ratio method proposed by the authors has been found to ...

  1. Identification of small-scale discontinuities based on dip-oriented gradient energy entropy coherence estimation

    Science.gov (United States)

    Peng, Da; Yin, Cheng

    2017-09-01

    Locating small-scale discontinuities is one of the most challenging geophysical tasks; these subtle geological features are significant since they are often associated with subsurface petroleum traps. Subtle faults, fractures, unconformities, reef textures, channel boundaries, thin-bed boundaries and other structural and stratigraphic discontinuities have subtle geological edges which may provide lateral variation in seismic expression. Among the different geophysical techniques available, 3D seismic discontinuity attributes are particularly useful for highlighting discontinuities in the seismic data. Traditional seismic discontinuity attributes are sensitive to noise and are not very appropriate for detecting small-scale discontinuities. Thus, we present a dip-oriented gradient energy entropy (DOGEE) coherence estimation method to detect subtle faults and structural features. The DOGEE coherence estimation method uses the gradient structure tensor (GST) algorithm to obtain local dip information and construct a gradient correlation matrix to calculate gradient energy entropy. The proposed DOGEE coherence estimation method is robust to noise, and also improves the clarity of fault edges. It is effective for small-scale discontinuity characterisation and interpretation.

  2. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2011-01-01

    In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator

  3. Reducing Bias and Increasing Precision by Adding Either a Pretest Measure of the Study Outcome or a Nonequivalent Comparison Group to the Basic Regression Discontinuity Design: An Example from Education

    Science.gov (United States)

    Tang, Yang; Cook, Thomas D.; Kisbu-Sakarya, Yasemin

    2015-01-01

    Regression discontinuity design (RD) has been widely used to produce reliable causal estimates. Researchers have validated the accuracy of RD design using within study comparisons (Cook, Shadish & Wong, 2008; Cook & Steiner, 2010; Shadish et al, 2011). Within study comparisons examines the validity of a quasi-experiment by comparing its…

  4. A discontinuous Poisson-Boltzmann equation with interfacial jump: homogenisation and residual error estimate.

    Science.gov (United States)

    Fellner, Klemens; Kovtunenko, Victor A

    2016-01-01

    A nonlinear Poisson-Boltzmann equation with inhomogeneous Robin type boundary conditions at the interface between two materials is investigated. The model describes the electrostatic potential generated by a vector of ion concentrations in a periodic multiphase medium with dilute solid particles. The key issue stems from interfacial jumps, which necessitate discontinuous solutions to the problem. Based on variational techniques, we derive the homogenisation of the discontinuous problem and establish a rigorous residual error estimate up to the first-order correction.

  5. -Error Estimates of the Extrapolated Crank-Nicolson Discontinuous Galerkin Approximations for Nonlinear Sobolev Equations

    Directory of Open Access Journals (Sweden)

    Lee HyunYoung

    2010-01-01

    Full Text Available We analyze discontinuous Galerkin methods with penalty terms, namely, symmetric interior penalty Galerkin methods, to solve nonlinear Sobolev equations. We construct finite element spaces on which we develop fully discrete approximations using extrapolated Crank-Nicolson method. We adopt an appropriate elliptic-type projection, which leads to optimal error estimates of discontinuous Galerkin approximations in both spatial direction and temporal direction.

  6. Block volume estimation from the discontinuity spacing measurements of mesozoic limestone quarries, Karaburun Peninsula, Turkey.

    Science.gov (United States)

    Elci, Hakan; Turk, Necdet

    2014-01-01

    Block volumes are generally estimated by analyzing the discontinuity spacing measurements obtained either from the scan lines placed over the rock exposures or the borehole cores. Discontinuity spacing measurements made at the Mesozoic limestone quarries in Karaburun Peninsula were used to estimate the average block volumes that could be produced from them using the suggested methods in the literature. The Block Quality Designation (BQD) ratio method proposed by the authors has been found to have given in the same order of the rock block volume to the volumetric joint count (J(v)) method. Moreover, dimensions of the 2378 blocks produced between the years of 2009 and 2011 in the working quarries have been recorded. Assuming, that each block surfaces is a discontinuity, the mean block volume (V(b)), the mean volumetric joint count (J(vb)) and the mean block shape factor of the blocks are determined and compared with the estimated mean in situ block volumes (V(in)) and volumetric joint count (J(vi)) values estimated from the in situ discontinuity measurements. The established relations are presented as a chart to be used in practice for estimating the mean volume of blocks that can be obtained from a quarry site by analyzing the rock mass discontinuity spacing measurements.

  7. Block Volume Estimation from the Discontinuity Spacing Measurements of Mesozoic Limestone Quarries, Karaburun Peninsula, Turkey

    Directory of Open Access Journals (Sweden)

    Hakan Elci

    2014-01-01

    Full Text Available Block volumes are generally estimated by analyzing the discontinuity spacing measurements obtained either from the scan lines placed over the rock exposures or the borehole cores. Discontinuity spacing measurements made at the Mesozoic limestone quarries in Karaburun Peninsula were used to estimate the average block volumes that could be produced from them using the suggested methods in the literature. The Block Quality Designation (BQD ratio method proposed by the authors has been found to have given in the same order of the rock block volume to the volumetric joint count (Jv method. Moreover, dimensions of the 2378 blocks produced between the years of 2009 and 2011 in the working quarries have been recorded. Assuming, that each block surfaces is a discontinuity, the mean block volume (Vb, the mean volumetric joint count (Jvb and the mean block shape factor of the blocks are determined and compared with the estimated mean in situ block volumes (Vin and volumetric joint count (Jvi values estimated from the in situ discontinuity measurements. The established relations are presented as a chart to be used in practice for estimating the mean volume of blocks that can be obtained from a quarry site by analyzing the rock mass discontinuity spacing measurements.

  8. Updated Design Standards and Guidance from the What Works Clearinghouse: Regression Discontinuity Designs and Cluster Designs

    Science.gov (United States)

    Cole, Russell; Deke, John; Seftor, Neil

    2016-01-01

    The What Works Clearinghouse (WWC) maintains design standards to identify rigorous, internally valid education research. As education researchers advance new methodologies, the WWC must revise its standards to include an assessment of the new designs. Recently, the WWC has revised standards for two emerging study designs: regression discontinuity…

  9. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2009-01-01

    In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

  10. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2010-01-01

    In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

  11. Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

    Science.gov (United States)

    Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

    2017-06-01

    A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

  12. Estimating the exceedance probability of rain rate by logistic regression

    Science.gov (United States)

    Chiu, Long S.; Kedem, Benjamin

    1990-01-01

    Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

  13. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha

    2014-12-08

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  14. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha; Huang, Jianhua Z.

    2014-01-01

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  15. Independent contrasts and PGLS regression estimators are equivalent.

    Science.gov (United States)

    Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

    2012-05-01

    We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

  16. Hierarchical Matching and Regression with Application to Photometric Redshift Estimation

    Science.gov (United States)

    Murtagh, Fionn

    2017-06-01

    This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or `photo-z' problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.

  17. The Effect of Merit Aid on Enrollment: A Regression Discontinuity Analysis of Iowa's National Scholars Award

    Science.gov (United States)

    Leeds, Daniel M.; DesJardins, Stephen L.

    2015-01-01

    The cost of attending college has risen steadily over the past 30 years, making financial aid an important determinant of college choice for many students and a subject of concern for colleges and state governments. In this paper, we estimate the effect of rule-based merit aid assignment on students' enrollment decisions at the University of Iowa.…

  18. A flexible fuzzy regression algorithm for forecasting oil consumption estimation

    International Nuclear Information System (INIS)

    Azadeh, A.; Khakestani, M.; Saberi, M.

    2009-01-01

    Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.

  19. On the estimation and testing of predictive panel regressions

    NARCIS (Netherlands)

    Karabiyik, H.; Westerlund, Joakim; Narayan, Paresh

    2016-01-01

    Hjalmarsson (2010) considers an OLS-based estimator of predictive panel regressions that is argued to be mixed normal under very general conditions. In a recent paper, Westerlund et al. (2016) show that while consistent, the estimator is generally not mixed normal, which invalidates standard normal

  20. Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

    Science.gov (United States)

    Gorgees, HazimMansoor; Mahdi, FatimahAssim

    2018-05-01

    This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

  1. Parameter Estimation for Improving Association Indicators in Binary Logistic Regression

    Directory of Open Access Journals (Sweden)

    Mahdi Bashiri

    2012-02-01

    Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.

  2. Small sample GEE estimation of regression parameters for longitudinal data.

    Science.gov (United States)

    Paul, Sudhir; Zhang, Xuemao

    2014-09-28

    Longitudinal (clustered) response data arise in many bio-statistical applications which, in general, cannot be assumed to be independent. Generalized estimating equation (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias adjusted GEE estimators of the regression parameters in longitudinal data are obtained when the number of subjects is small. One is based on a bias correction, and the other is based on a bias reduction. Simulations show that the performances of both the bias-corrected methods are similar in terms of bias, efficiency, coverage probability, average coverage length, impact of misspecification of correlation structure, and impact of cluster size on bias correction. Both these methods show superior properties over the GEE estimates for small samples. Further, analysis of data involving a small number of subjects also shows improvement in bias, MSE, standard error, and length of the confidence interval of the estimates by the two bias adjusted methods over the GEE estimates. For small to moderate sample sizes (N ≤50), either of the bias-corrected methods GEEBc and GEEBr can be used. However, the method GEEBc should be preferred over GEEBr, as the former is computationally easier. For large sample sizes, the GEE method can be used. Copyright © 2014 John Wiley & Sons, Ltd.

  3. Performance of a New Restricted Biased Estimator in Logistic Regression

    Directory of Open Access Journals (Sweden)

    Yasin ASAR

    2017-12-01

    Full Text Available It is known that the variance of the maximum likelihood estimator (MLE inflates when the explanatory variables are correlated. This situation is called the multicollinearity problem. As a result, the estimations of the model may not be trustful. Therefore, this paper introduces a new restricted estimator (RLTE that may be applied to get rid of the multicollinearity when the parameters lie in some linear subspace  in logistic regression. The mean squared errors (MSE and the matrix mean squared errors (MMSE of the estimators considered in this paper are given. A Monte Carlo experiment is designed to evaluate the performances of the proposed estimator, the restricted MLE (RMLE, MLE and Liu-type estimator (LTE. The criterion of performance is chosen to be MSE. Moreover, a real data example is presented. According to the results, proposed estimator has better performance than MLE, RMLE and LTE.

  4. Estimating monotonic rates from biological data using local linear regression.

    Science.gov (United States)

    Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

    2017-03-01

    Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

  5. On the robust nonparametric regression estimation for a functional regressor

    OpenAIRE

    Azzedine , Nadjia; Laksaci , Ali; Ould-Saïd , Elias

    2009-01-01

    On the robust nonparametric regression estimation for a functional regressor correspondance: Corresponding author. (Ould-Said, Elias) (Azzedine, Nadjia) (Laksaci, Ali) (Ould-Said, Elias) Departement de Mathematiques--> , Univ. Djillali Liabes--> , BP 89--> , 22000 Sidi Bel Abbes--> - ALGERIA (Azzedine, Nadjia) Departement de Mathema...

  6. Two biased estimation techniques in linear regression: Application to aircraft

    Science.gov (United States)

    Klein, Vladislav

    1988-01-01

    Several ways for detection and assessment of collinearity in measured data are discussed. Because data collinearity usually results in poor least squares estimates, two estimation techniques which can limit a damaging effect of collinearity are presented. These two techniques, the principal components regression and mixed estimation, belong to a class of biased estimation techniques. Detection and assessment of data collinearity and the two biased estimation techniques are demonstrated in two examples using flight test data from longitudinal maneuvers of an experimental aircraft. The eigensystem analysis and parameter variance decomposition appeared to be a promising tool for collinearity evaluation. The biased estimators had far better accuracy than the results from the ordinary least squares technique.

  7. Online and Batch Supervised Background Estimation via L1 Regression

    KAUST Repository

    Dutta, Aritra

    2017-11-23

    We propose a surprisingly simple model for supervised video background estimation. Our model is based on $\\\\ell_1$ regression. As existing methods for $\\\\ell_1$ regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures.

  8. Online and Batch Supervised Background Estimation via L1 Regression

    KAUST Repository

    Dutta, Aritra; Richtarik, Peter

    2017-01-01

    We propose a surprisingly simple model for supervised video background estimation. Our model is based on $\\ell_1$ regression. As existing methods for $\\ell_1$ regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures.

  9. Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

    Directory of Open Access Journals (Sweden)

    Qiutong Jin

    2016-06-01

    Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

  10. Optimized support vector regression for drilling rate of penetration estimation

    Science.gov (United States)

    Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa

    2015-12-01

    In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.

  11. Nonparametric Regression Estimation for Multivariate Null Recurrent Processes

    Directory of Open Access Journals (Sweden)

    Biqing Cai

    2015-04-01

    Full Text Available This paper discusses nonparametric kernel regression with the regressor being a \\(d\\-dimensional \\(\\beta\\-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate \\(\\sqrt{n(Th^{d}}\\, where \\(n(T\\ is the number of regenerations for a \\(\\beta\\-null recurrent process and the limiting distribution (with proper normalization is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.

  12. Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Changwei Ma

    2015-01-01

    Full Text Available Discrete Fourier transform- (DFT- based maximum likelihood (ML algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB, which is dependent on the number of DFT points. However, its mean square error (MSE performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR, least squares SVR (LS-SVR can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known.

  13. Estimation of adjusted rate differences using additive negative binomial regression.

    Science.gov (United States)

    Donoghoe, Mark W; Marschner, Ian C

    2016-08-15

    Rate differences are an important effect measure in biostatistics and provide an alternative perspective to rate ratios. When the data are event counts observed during an exposure period, adjusted rate differences may be estimated using an identity-link Poisson generalised linear model, also known as additive Poisson regression. A problem with this approach is that the assumption of equality of mean and variance rarely holds in real data, which often show overdispersion. An additive negative binomial model is the natural alternative to account for this; however, standard model-fitting methods are often unable to cope with the constrained parameter space arising from the non-negativity restrictions of the additive model. In this paper, we propose a novel solution to this problem using a variant of the expectation-conditional maximisation-either algorithm. Our method provides a reliable way to fit an additive negative binomial regression model and also permits flexible generalisations using semi-parametric regression functions. We illustrate the method using a placebo-controlled clinical trial of fenofibrate treatment in patients with type II diabetes, where the outcome is the number of laser therapy courses administered to treat diabetic retinopathy. An R package is available that implements the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  14. Genomic breeding value estimation using nonparametric additive regression models

    Directory of Open Access Journals (Sweden)

    Solberg Trygve

    2009-01-01

    Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.

  15. Image Jacobian Matrix Estimation Based on Online Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Shangqin Mao

    2012-10-01

    Full Text Available Research into robotics visual servoing is an important area in the field of robotics. It has proven difficult to achieve successful results for machine vision and robotics in unstructured environments without using any a priori camera or kinematic models. In uncalibrated visual servoing, image Jacobian matrix estimation methods can be divided into two groups: the online method and the offline method. The offline method is not appropriate for most natural environments. The online method is robust but rough. Moreover, if the images feature configuration changes, it needs to restart the approximating procedure. A novel approach based on an online support vector regression (OL-SVR algorithm is proposed which overcomes the drawbacks and combines the virtues just mentioned.

  16. L2-Error Estimates of the Extrapolated Crank-Nicolson Discontinuous Galerkin Approximations for Nonlinear Sobolev Equations

    Directory of Open Access Journals (Sweden)

    Hyun Young Lee

    2010-01-01

    Full Text Available We analyze discontinuous Galerkin methods with penalty terms, namely, symmetric interior penalty Galerkin methods, to solve nonlinear Sobolev equations. We construct finite element spaces on which we develop fully discrete approximations using extrapolated Crank-Nicolson method. We adopt an appropriate elliptic-type projection, which leads to optimal ℓ∞(L2 error estimates of discontinuous Galerkin approximations in both spatial direction and temporal direction.

  17. Is There Evidence for Systematic Upcoding of ASA Physical Status Coincident with Payer Incentives? A Regression Discontinuity Analysis of the National Anesthesia Clinical Outcomes Registry.

    Science.gov (United States)

    Schonberger, Robert B; Dutton, Richard P; Dai, Feng

    2016-01-01

    Modifications in physician billing patterns have been shown to occur in response to payer incentives, but the phenomenon remains largely unexplored in billing for anesthesia services. Within the field of anesthesiology, Medicare's policy not to provide additional reimbursement for higher ASA physical status scores contrasts with the practices of most private payers, and this pattern of reimbursement introduces a change in billing incentives once patients attain Medicare eligibility. We hypothesized that, coincident with the onset of widespread Medicare eligibility at age 65 years, a discontinuity in reported ASA physical status scores would be observed after controlling for the underlying trend of increasing ASA physical status scores with age. This phenomenon would manifest as a pattern of upcoding of ASA physical status scores for patients younger than 65 years that would become less common in patients age 65 years and older. Using data on age, sex, ASA physical status scores, and type of surgery from the National Anesthesia Clinical Outcomes Registry, we used a quasi-experimental regression discontinuity design to analyze whether there was evidence for a discontinuity in reported ASA physical status scores occurring at age 65 years for the nondeferrable anesthesia services accompanying hip, femur, or lower leg fracture repair. A total of 49,850 records were analyzed. In models designed to detect regression discontinuity at 65 years of age, neither the binary variable "age ≥ 65" nor the interaction term of age × age ≥ 65 was a statistically significant predictor of the outcome of ASA physical status score. The statistical inference was unchanged when ASA physical status scores were reclassified as a binary outcome (I-II vs III-V) and when different bandwidths around age 65 years were used. To test the validity of our study design for detecting regression discontinuity, simulations of the occurrence of deliberate upcoding of ASA physical status scores

  18. Energy dependent mesh adaptivity of discontinuous isogeometric discrete ordinate methods with dual weighted residual error estimators

    Science.gov (United States)

    Owens, A. R.; Kópházi, J.; Welch, J. A.; Eaton, M. D.

    2017-04-01

    In this paper a hanging-node, discontinuous Galerkin, isogeometric discretisation of the multigroup, discrete ordinates (SN) equations is presented in which each energy group has its own mesh. The equations are discretised using Non-Uniform Rational B-Splines (NURBS), which allows the coarsest mesh to exactly represent the geometry for a wide range of engineering problems of interest; this would not be the case using straight-sided finite elements. Information is transferred between meshes via the construction of a supermesh. This is a non-trivial task for two arbitrary meshes, but is significantly simplified here by deriving every mesh from a common coarsest initial mesh. In order to take full advantage of this flexible discretisation, goal-based error estimators are derived for the multigroup, discrete ordinates equations with both fixed (extraneous) and fission sources, and these estimators are used to drive an adaptive mesh refinement (AMR) procedure. The method is applied to a variety of test cases for both fixed and fission source problems. The error estimators are found to be extremely accurate for linear NURBS discretisations, with degraded performance for quadratic discretisations owing to a reduction in relative accuracy of the "exact" adjoint solution required to calculate the estimators. Nevertheless, the method seems to produce optimal meshes in the AMR process for both linear and quadratic discretisations, and is ≈×100 more accurate than uniform refinement for the same amount of computational effort for a 67 group deep penetration shielding problem.

  19. Regression and kriging analysis for grid power factor estimation

    Directory of Open Access Journals (Sweden)

    Rajesh Guntaka

    2014-12-01

    Full Text Available The measurement of power factor (PF in electrical utility grids is a mainstay of load balancing and is also a critical element of transmission and distribution efficiency. The measurement of PF dates back to the earliest periods of electrical power distribution to public grids. In the wide-area distribution grid, measurement of current waveforms is trivial and may be accomplished at any point in the grid using a current tap transformer. However, voltage measurement requires reference to ground and so is more problematic and measurements are normally constrained to points that have ready and easy access to a ground source. We present two mathematical analysis methods based on kriging and linear least square estimation (LLSE (regression to derive PF at nodes with unknown voltages that are within a perimeter of sample nodes with ground reference across a selected power grid. Our results indicate an error average of 1.884% that is within acceptable tolerances for PF measurements that are used in load balancing tasks.

  20. Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

    Science.gov (United States)

    Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Leclère, Vincent; Salmon, Joseph

    2017-10-01

    In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider ℓ 1 penalty to enforce sparsity in such scenarios. Sparsity enforcing methods, the Lasso being a canonical example, are popular candidates to address high dimension. For efficiency, they rely on tuning a parameter trading data fitting versus sparsity. For the Lasso theory to hold this tuning parameter should be proportional to the noise level, yet the latter is often unknown in practice. A possible remedy is to jointly optimize over the regression parameter as well as over the noise level. This has been considered under several names in the literature: Scaled-Lasso, Square-root Lasso, Concomitant Lasso estimation for instance, and could be of interest for uncertainty quantification. In this work, after illustrating numerical difficulties for the Concomitant Lasso formulation, we propose a modification we coined Smoothed Concomitant Lasso, aimed at increasing numerical stability. We propose an efficient and accurate solver leading to a computational cost no more expensive than the one for the Lasso. We leverage on standard ingredients behind the success of fast Lasso solvers: a coordinate descent algorithm, combined with safe screening rules to achieve speed efficiency, by eliminating early irrelevant features.

  1. Regression and Sparse Regression Methods for Viscosity Estimation of Acid Milk From it’s Sls Features

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann

    2012-01-01

    Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... with sparse LAR, lasso and Elastic Net (EN) sparse regression methods. Due to the inconsistent measurement condition, Locally Weighted Scatter plot Smoothing (Loess) has been employed to alleviate the undesired variation in the estimated viscosity. The experimental results of applying different methods show...

  2. The Collinearity Free and Bias Reduced Regression Estimation Project: The Theory of Normalization Ridge Regression. Report No. 2.

    Science.gov (United States)

    Bulcock, J. W.; And Others

    Multicollinearity refers to the presence of highly intercorrelated independent variables in structural equation models, that is, models estimated by using techniques such as least squares regression and maximum likelihood. There is a problem of multicollinearity in both the natural and social sciences where theory formulation and estimation is in…

  3. Early discontinuation

    DEFF Research Database (Denmark)

    Hansen, Dorte Gilså; Felde, Lina; Gichangi, Anthony

    2007-01-01

    prevalence and rate of early discontinuation of different drugs consisting of, in this study, lipid-lowering drugs, antihypertensive drugs, antidepressants, antidiabetics and drugs against osteoporosis. Material and methods This was a register study based on prescription data covering a 4-year period...... and consisting of 470,000 citizens. For each practice and group of drug, a 1-year prevalence for 2002 and the rate of early discontinuation among new users in 2002-2003 were estimated. Early discontinuation was defined as no prescriptions during the second half-year following the first prescription....... There was a positive association between the prevalence of prescribing for the specific drugs studied (antidepressants, antidiabetics, drugs against osteoporosis and lipid-lowering drugs) and early discontinuation (r = 0.29 -0.44), but not for anti-hypertensive drugs. The analysis of the association between prevalence...

  4. The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

    Science.gov (United States)

    Ariffin, Syaiba Balqish; Midi, Habshah

    2014-06-01

    This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity.

  5. Bias in regression coefficient estimates upon different treatments of ...

    African Journals Online (AJOL)

    MS and PW consistently overestimated the population parameter. EM and RI, on the other hand, tended to consistently underestimate the population parameter under non-monotonic pattern. Keywords: Missing data, bias, regression, percent missing, non-normality, missing pattern > East African Journal of Statistics Vol.

  6. Stellar atmospheric parameter estimation using Gaussian process regression

    Science.gov (United States)

    Bu, Yude; Pan, Jingchang

    2015-02-01

    As is well known, it is necessary to derive stellar parameters from massive amounts of spectral data automatically and efficiently. However, in traditional automatic methods such as artificial neural networks (ANNs) and kernel regression (KR), it is often difficult to optimize the algorithm structure and determine the optimal algorithm parameters. Gaussian process regression (GPR) is a recently developed method that has been proven to be capable of overcoming these difficulties. Here we apply GPR to derive stellar atmospheric parameters from spectra. Through evaluating the performance of GPR on Sloan Digital Sky Survey (SDSS) spectra, Medium resolution Isaac Newton Telescope Library of Empirical Spectra (MILES) spectra, ELODIE spectra and the spectra of member stars of galactic globular clusters, we conclude that GPR can derive stellar parameters accurately and precisely, especially when we use data preprocessed with principal component analysis (PCA). We then compare the performance of GPR with that of several widely used regression methods (ANNs, support-vector regression and KR) and find that with GPR it is easier to optimize structures and parameters and more efficient and accurate to extract atmospheric parameters.

  7. Estimation of Production KWS Maize Hybrids Using Nonlinear Regression

    Directory of Open Access Journals (Sweden)

    Florica MORAR

    2018-06-01

    Full Text Available This article approaches the model of non-linear regression and the method of smallest squares with examples, including calculations for the model of logarithmic function. This required data obtained from a study which involved the observation of the phases of growth and development in KWS maize hybrids in order to analyze the influence of the MMB quality indicator on grain production per hectare.

  8. Saving Teens: Using a Policy Discontinuity to Estimate the Effects of Medicaid Eligibility

    OpenAIRE

    Bruce D. Meyer; Laura R. Wherry

    2012-01-01

    This paper uses a policy discontinuity to identify the immediate and long-term effects of public health insurance coverage during childhood. Our identification strategy exploits a unique feature of several early Medicaid expansions that extended eligibility only to children born after September 30, 1983. This feature resulted in a large discontinuity in the lifetime years of Medicaid eligibility of children at this birthdate cutoff. Those with family incomes at or just below the poverty line ...

  9. Regularized Regression and Density Estimation based on Optimal Transport

    KAUST Repository

    Burger, M.; Franek, M.; Schonlieb, C.-B.

    2012-01-01

    for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations

  10. Estimating life expectancies for US small areas: a regression framework

    Science.gov (United States)

    Congdon, Peter

    2014-01-01

    Analysis of area mortality variations and estimation of area life tables raise methodological questions relevant to assessing spatial clustering, and socioeconomic inequalities in mortality. Existing small area analyses of US life expectancy variation generally adopt ad hoc amalgamations of counties to alleviate potential instability of mortality rates involved in deriving life tables, and use conventional life table analysis which takes no account of correlated mortality for adjacent areas or ages. The alternative strategy here uses structured random effects methods that recognize correlations between adjacent ages and areas, and allows retention of the original county boundaries. This strategy generalizes to include effects of area category (e.g. poverty status, ethnic mix), allowing estimation of life tables according to area category, and providing additional stabilization of estimated life table functions. This approach is used here to estimate stabilized mortality rates, derive life expectancies in US counties, and assess trends in clustering and in inequality according to county poverty category.

  11. Analysis of an a posteriori error estimator for the transport equation with SN and discontinuous Galerkin discretizations

    International Nuclear Information System (INIS)

    Fournier, D.; Le Tellier, R.; Suteau, C.

    2011-01-01

    We present an error estimator for the S N neutron transport equation discretized with an arbitrary high-order discontinuous Galerkin method. As a starting point, the estimator is obtained for conforming Cartesian meshes with a uniform polynomial order for the trial space then adapted to deal with non-conforming meshes and a variable polynomial order. Some numerical tests illustrate the properties of the estimator and its limitations. Finally, a simple shielding benchmark is analyzed in order to show the relevance of the estimator in an adaptive process.

  12. Allelic drop-out probabilities estimated by logistic regression

    DEFF Research Database (Denmark)

    Tvedebrink, Torben; Eriksen, Poul Svante; Asplund, Maria

    2012-01-01

    We discuss the model for estimating drop-out probabilities presented by Tvedebrink et al. [7] and the concerns, that have been raised. The criticism of the model has demonstrated that the model is not perfect. However, the model is very useful for advanced forensic genetic work, where allelic drop-out...... is occurring. With this discussion, we hope to improve the drop-out model, so that it can be used for practical forensic genetics and stimulate further discussions. We discuss how to estimate drop-out probabilities when using a varying number of PCR cycles and other experimental conditions....

  13. On the estimation of the degree of regression polynomial

    International Nuclear Information System (INIS)

    Toeroek, Cs.

    1997-01-01

    The mathematical functions most commonly used to model curvature in plots are polynomials. Generally, the higher the degree of the polynomial, the more complex is the trend that its graph can represent. We propose a new statistical-graphical approach based on the discrete projective transformation (DPT) to estimating the degree of polynomial that adequately describes the trend in the plot

  14. Regularized Regression and Density Estimation based on Optimal Transport

    KAUST Repository

    Burger, M.

    2012-03-11

    The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability densities from discrete samples based on a variational regularization method with the Wasserstein metric as a data fidelity. The approach allows a unified treatment of discrete and continuous probability measures and is hence attractive for various tasks. In particular, the variational model for special regularization functionals yields a natural method for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations and provide a detailed analysis. Moreover, we compute special self-similar solutions for standard regularization functionals and we discuss several computational approaches and results. © 2012 The Author(s).

  15. The effect of age at school entry on college admission and earnings: a regression-discontinuity approach

    Directory of Open Access Journals (Sweden)

    Rafael Matta

    2016-07-01

    Full Text Available Abstract This paper provides evidence of the effect of age at school entry on college admission and earnings. It does so by exploiting a number of features in the application process to one of the major flagship universities in Brazil. By comparing applicants with different ages at school entry depending on whether they were born on December 31 or on January 1, our estimates show that applicants who delayed first-grade enrollment present higher aptitude test scores and probability of admission. Our results further suggest that advantaged applicants also earn more early in their careers. JEL Classification: I21, J24

  16. Nonparametric Estimation of Regression Parameters in Measurement Error Models

    Czech Academy of Sciences Publication Activity Database

    Ehsanes Saleh, A.K.M.D.; Picek, J.; Kalina, Jan

    2009-01-01

    Roč. 67, č. 2 (2009), s. 177-200 ISSN 0026-1424 Grant - others:GA AV ČR(CZ) IAA101120801; GA MŠk(CZ) LC06024 Institutional research plan: CEZ:AV0Z10300504 Keywords : asymptotic relative efficiency(ARE) * asymptotic theory * emaculate mode * Me model * R-estimation * Reliabilty ratio(RR) Subject RIV: BB - Applied Statistics, Operational Research

  17. A SAS-macro for estimation of the cumulative incidence using Poisson regression

    DEFF Research Database (Denmark)

    Waltoft, Berit Lindum

    2009-01-01

    the hazard rates, and the hazard rates are often estimated by the Cox regression. This procedure may not be suitable for large studies due to limited computer resources. Instead one uses Poisson regression, which approximates the Cox regression. Rosthøj et al. presented a SAS-macro for the estimation...... of the cumulative incidences based on the Cox regression. I present the functional form of the probabilities and variances when using piecewise constant hazard rates and a SAS-macro for the estimation using Poisson regression. The use of the macro is demonstrated through examples and compared to the macro presented...

  18. Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

    Science.gov (United States)

    Yelland, Lisa N; Salter, Amy B; Ryan, Philip

    2011-10-15

    Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

  19. Tightness of M-estimators for multiple linear regression in time series

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Bent

    We show tightness of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semi-continuous and sufficiently large for large argument: Particular cases are the Huber-skip and quantile regression. Tightness requires...

  20. Comparison of some biased estimation methods (including ordinary subset regression) in the linear model

    Science.gov (United States)

    Sidik, S. M.

    1975-01-01

    Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.

  1. The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

    Science.gov (United States)

    Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

    2014-09-01

    In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

  2. truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

    Directory of Open Access Journals (Sweden)

    Maria Karlsson

    2014-05-01

    Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

  3. The efficiency of modified jackknife and ridge type regression estimators: a comparison

    Directory of Open Access Journals (Sweden)

    Sharad Damodar Gore

    2008-09-01

    Full Text Available A common problem in multiple regression models is multicollinearity, which produces undesirable effects on the least squares estimator. To circumvent this problem, two well known estimation procedures are often suggested in the literature. They are Generalized Ridge Regression (GRR estimation suggested by Hoerl and Kennard iteb8 and the Jackknifed Ridge Regression (JRR estimation suggested by Singh et al. iteb13. The GRR estimation leads to a reduction in the sampling variance, whereas, JRR leads to a reduction in the bias. In this paper, we propose a new estimator namely, Modified Jackknife Ridge Regression Estimator (MJR. It is based on the criterion that combines the ideas underlying both the GRR and JRR estimators. We have investigated standard properties of this new estimator. From a simulation study, we find that the new estimator often outperforms the LASSO, and it is superior to both GRR and JRR estimators, using the mean squared error criterion. The conditions under which the MJR estimator is better than the other two competing estimators have been investigated.

  4. Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

    Science.gov (United States)

    Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

    2012-05-01

    Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  5. Estimating nonlinear selection gradients using quadratic regression coefficients: double or nothing?

    Science.gov (United States)

    Stinchcombe, John R; Agrawal, Aneil F; Hohenlohe, Paul A; Arnold, Stevan J; Blows, Mark W

    2008-09-01

    The use of regression analysis has been instrumental in allowing evolutionary biologists to estimate the strength and mode of natural selection. Although directional and correlational selection gradients are equal to their corresponding regression coefficients, quadratic regression coefficients must be doubled to estimate stabilizing/disruptive selection gradients. Based on a sample of 33 papers published in Evolution between 2002 and 2007, at least 78% of papers have not doubled quadratic regression coefficients, leading to an appreciable underestimate of the strength of stabilizing and disruptive selection. Proper treatment of quadratic regression coefficients is necessary for estimation of fitness surfaces and contour plots, canonical analysis of the gamma matrix, and modeling the evolution of populations on an adaptive landscape.

  6. Estimation of pyrethroid pesticide intake using regression modeling of food groups based on composite dietary samples

    Data.gov (United States)

    U.S. Environmental Protection Agency — Population-based estimates of pesticide intake are needed to characterize exposure for particular demographic groups based on their dietary behaviors. Regression...

  7. Simultaneous Estimation of Regression Functions for Marine Corps Technical Training Specialties.

    Science.gov (United States)

    Dunbar, Stephen B.; And Others

    This paper considers the application of Bayesian techniques for simultaneous estimation to the specification of regression weights for selection tests used in various technical training courses in the Marine Corps. Results of a method for m-group regression developed by Molenaar and Lewis (1979) suggest that common weights for training courses…

  8. An evaluation of regression methods to estimate nutritional condition of canvasbacks and other water birds

    Science.gov (United States)

    Sparling, D.W.; Barzen, J.A.; Lovvorn, J.R.; Serie, J.R.

    1992-01-01

    Regression equations that use mensural data to estimate body condition have been developed for several water birds. These equations often have been based on data that represent different sexes, age classes, or seasons, without being adequately tested for intergroup differences. We used proximate carcass analysis of 538 adult and juvenile canvasbacks (Aythya valisineria ) collected during fall migration, winter, and spring migrations in 1975-76 and 1982-85 to test regression methods for estimating body condition.

  9. Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

    Science.gov (United States)

    Ding, A Adam; Wu, Hulin

    2014-10-01

    We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

  10. [Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

    Science.gov (United States)

    Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

    2017-03-10

    To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

  11. Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation

    DEFF Research Database (Denmark)

    Alan, Sule; Honore, Bo E.; Hu, Luojia

    2014-01-01

    This paper constructs estimators for panel data regression models with individual speci…fic heterogeneity and two–sided censoring and truncation. Following Powell (1986) the estimation strategy is based on moment conditions constructed from re–censored or re–truncated residuals. While these moment...

  12. Semi-parametric estimation of random effects in a logistic regression model using conditional inference

    DEFF Research Database (Denmark)

    Petersen, Jørgen Holm

    2016-01-01

    This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...

  13. Asymptotic normality of kernel estimator of $\\psi$-regression function for functional ergodic data

    OpenAIRE

    Laksaci ALI; Benziadi Fatima; Gheriballak Abdelkader

    2016-01-01

    In this paper we consider the problem of the estimation of the $\\psi$-regression function when the covariates take values in an infinite dimensional space. Our main aim is to establish, under a stationary ergodic process assumption, the asymptotic normality of this estimate.

  14. Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

    Science.gov (United States)

    Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

    2017-04-01

    Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

  15. The importance of the chosen technique to estimate diffuse solar radiation by means of regression

    Energy Technology Data Exchange (ETDEWEB)

    Arslan, Talha; Altyn Yavuz, Arzu [Department of Statistics. Science and Literature Faculty. Eskisehir Osmangazi University (Turkey)], email: mtarslan@ogu.edu.tr, email: aaltin@ogu.edu.tr; Acikkalp, Emin [Department of Mechanical and Manufacturing Engineering. Engineering Faculty. Bilecik University (Turkey)], email: acikkalp@gmail.com

    2011-07-01

    The Ordinary Least Squares (OLS) method is one of the most frequently used for estimation of diffuse solar radiation. The data set must provide certain assumptions for the OLS method to work. The most important is that the regression equation offered by OLS error terms must fit within the normal distribution. Utilizing an alternative robust estimator to get parameter estimations is highly effective in solving problems where there is a lack of normal distribution due to the presence of outliers or some other factor. The purpose of this study is to investigate the value of the chosen technique for the estimation of diffuse radiation. This study described alternative robust methods frequently used in applications and compared them with the OLS method. Making a comparison of the data set analysis of the OLS and that of the M Regression (Huber, Andrews and Tukey) techniques, it was study found that robust regression techniques are preferable to OLS because of the smoother explanation values.

  16. On the Choice of Difference Sequence in a Unified Framework for Variance Estimation in Nonparametric Regression

    KAUST Repository

    Dai, Wenlin; Tong, Tiejun; Zhu, Lixing

    2017-01-01

    Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.

  17. Robust estimation for homoscedastic regression in the secondary analysis of case-control data

    KAUST Repository

    Wei, Jiawei

    2012-12-04

    Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

  18. On the Choice of Difference Sequence in a Unified Framework for Variance Estimation in Nonparametric Regression

    KAUST Repository

    Dai, Wenlin

    2017-09-01

    Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.

  19. Robust estimation for homoscedastic regression in the secondary analysis of case-control data

    KAUST Repository

    Wei, Jiawei; Carroll, Raymond J.; Mü ller, Ursula U.; Keilegom, Ingrid Van; Chatterjee, Nilanjan

    2012-01-01

    Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

  20. Estimating integrated variance in the presence of microstructure noise using linear regression

    Science.gov (United States)

    Holý, Vladimír

    2017-07-01

    Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.

  1. Regression estimators for generic health-related quality of life and quality-adjusted life years.

    Science.gov (United States)

    Basu, Anirban; Manca, Andrea

    2012-01-01

    To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

  2. Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions.

    Science.gov (United States)

    Rativa, Diego; Fernandes, Bruno J T; Roque, Alexandre

    2018-01-01

    Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care.

  3. Estimation of Covariance Matrix on Bi-Response Longitudinal Data Analysis with Penalized Spline Regression

    Science.gov (United States)

    Islamiyati, A.; Fatmawati; Chamidah, N.

    2018-03-01

    The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix based on the penalized spline regression model. Penalized spline involves knot points and smoothing parameters simultaneously in controlling the smoothness of the curve. Based on our simulation study, the estimated regression model of the weighted penalized spline with covariance matrix gives a smaller error value compared to the error of the model without covariance matrix.

  4. Generalized allometric regression to estimate biomass of Populus in short-rotation coppice

    Energy Technology Data Exchange (ETDEWEB)

    Ben Brahim, Mohammed; Gavaland, Andre; Cabanettes, Alain [INRA Centre de Toulouse, Castanet-Tolosane Cedex (France). Unite Agroforesterie et Foret Paysanne

    2000-07-01

    Data from four different stands were combined to establish a single generalized allometric equation to estimate above-ground biomass of individual Populus trees grown on short-rotation coppice. The generalized model was performed using diameter at breast height, the mean diameter and the mean height of each site as dependent variables and then compared with the stand-specific regressions using F-test. Results showed that this single regression estimates tree biomass well at each stand and does not introduce bias with increasing diameter.

  5. Regression tools for CO2 inversions: application of a shrinkage estimator to process attribution

    International Nuclear Information System (INIS)

    Shaby, Benjamin A.; Field, Christopher B.

    2006-01-01

    In this study we perform an atmospheric inversion based on a shrinkage estimator. This method is used to estimate surface fluxes of CO 2 , first partitioned according to constituent geographic regions, and then according to constituent processes that are responsible for the total flux. Our approach differs from previous approaches in two important ways. The first is that the technique of linear Bayesian inversion is recast as a regression problem. Seen as such, standard regression tools are employed to analyse and reduce errors in the resultant estimates. A shrinkage estimator, which combines standard ridge regression with the linear 'Bayesian inversion' model, is introduced. This method introduces additional bias into the model with the aim of reducing variance such that errors are decreased overall. Compared with standard linear Bayesian inversion, the ridge technique seems to reduce both flux estimation errors and prediction errors. The second divergence from previous studies is that instead of dividing the world into geographically distinct regions and estimating the CO 2 flux in each region, the flux space is divided conceptually into processes that contribute to the total global flux. Formulating the problem in this manner adds to the interpretability of the resultant estimates and attempts to shed light on the problem of attributing sources and sinks to their underlying mechanisms

  6. Robust best linear estimation for regression analysis using surrogate and instrumental variables.

    Science.gov (United States)

    Wang, C Y

    2012-04-01

    We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case-control study.

  7. On the degrees of freedom of reduced-rank estimators in multivariate regression.

    Science.gov (United States)

    Mukherjee, A; Chen, K; Wang, N; Zhu, J

    We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions may be larger than the sample size. The derived analytical form facilitates the investigation of theoretical properties and provides new insights into the empirical behaviour of the degrees of freedom. In particular, we examine the differences and connections between the proposed estimator and a commonly-used naive estimator. The use of the proposed estimator leads to efficient and accurate prediction risk estimation and model selection, as demonstrated by simulation studies and a data example.

  8. Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

    Science.gov (United States)

    Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

    2018-06-01

    Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

  9. Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

    Science.gov (United States)

    Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

    2018-03-01

    Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

  10. Estimation of Geographically Weighted Regression Case Study on Wet Land Paddy Productivities in Tulungagung Regency

    Directory of Open Access Journals (Sweden)

    Danang Ariyanto

    2017-11-01

    Full Text Available Regression is a method connected independent variable and dependent variable with estimation parameter as an output. Principal problem in this method is its application in spatial data. Geographically Weighted Regression (GWR method used to solve the problem. GWR  is a regression technique that extends the traditional regression framework by allowing the estimation of local rather than global parameters. In other words, GWR runs a regression for each location, instead of a sole regression for the entire study area. The purpose of this research is to analyze the factors influencing wet land paddy productivities in Tulungagung Regency. The methods used in this research is  GWR using cross validation  bandwidth and weighted by adaptive Gaussian kernel fungtion.This research using  4 variables which are presumed affecting the wet land paddy productivities such as:  the rate of rainfall(X1, the average cost of fertilizer per hectare(X2, the average cost of pestisides per hectare(X3 and Allocation of subsidized NPK fertilizer of food crops sub-sector(X4. Based on the result, X1, X2, X3 and X4  has a different effect on each Distric. So, to improve the productivity of wet land paddy in Tulungagung Regency required a special policy based on the GWR model in each distric.

  11. Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

    Science.gov (United States)

    Amalia, Junita; Purhadi, Otok, Bambang Widjanarko

    2017-11-01

    Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.

  12. Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods

    Directory of Open Access Journals (Sweden)

    Dick Apronti

    2016-12-01

    Full Text Available Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effective alternative to taking traffic counts. This is because traditional traffic counts are expensive and impractical for low priority roads. The purpose of this paper is to present the development of two alternative means of cost-effectively estimating traffic volumes for low volume roads in Wyoming and to make recommendations for their implementation. The study methodology involves reviewing existing studies, identifying data sources, and carrying out the model development. The utility of the models developed were then verified by comparing actual traffic volumes to those predicted by the model. The study resulted in two regression models that are inexpensive and easy to implement. The first regression model was a linear regression model that utilized pavement type, access to highways, predominant land use types, and population to estimate traffic volume. In verifying the model, an R2 value of 0.64 and a root mean square error of 73.4% were obtained. The second model was a logistic regression model that identified the level of traffic on roads using five thresholds or levels. The logistic regression model was verified by estimating traffic volume thresholds and determining the percentage of roads that were accurately classified as belonging to the given thresholds. For the five thresholds, the percentage of roads classified correctly ranged from 79% to 88%. In conclusion, the verification of the models indicated both model types to be useful for accurate and cost-effective estimation of traffic volumes for low volume Wyoming roads. The models developed were recommended for use in traffic volume estimations for low volume roads in pavement management and environmental impact assessment studies.

  13. A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

    Science.gov (United States)

    Huang, C.; Townshend, J.R.G.

    2003-01-01

    A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al . (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.

  14. Adding a Parameter Increases the Variance of an Estimated Regression Function

    Science.gov (United States)

    Withers, Christopher S.; Nadarajah, Saralees

    2011-01-01

    The linear regression model is one of the most popular models in statistics. It is also one of the simplest models in statistics. It has received applications in almost every area of science, engineering and medicine. In this article, the authors show that adding a predictor to a linear model increases the variance of the estimated regression…

  15. Early cost estimating for road construction projects using multiple regression techniques

    Directory of Open Access Journals (Sweden)

    Ibrahim Mahamid

    2011-12-01

    Full Text Available The objective of this study is to develop early cost estimating models for road construction projects using multiple regression techniques, based on 131 sets of data collected in the West Bank in Palestine. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for the required regression model could be easily extracted from sketches or scope definition of the project. 11 regression models are developed to estimate the total cost of road construction project in US dollar; 5 of them include bid quantities as input variables and 6 include road length and road width. The coefficient of determination r2 for the developed models is ranging from 0.92 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE of the developed regression models are ranging from 13% to 31%, the results compare favorably with past researches which have shown that the estimate accuracy in the early stages of a project is between ±25% and ±50%.

  16. The limiting behavior of the estimated parameters in a misspecified random field regression model

    DEFF Research Database (Denmark)

    Dahl, Christian Møller; Qin, Yu

    This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection of n...

  17. Targeted Funding for Educationally Disadvantaged Students: A Regression Discontinuity Estimate of the Impact on High School Student Achievement

    Science.gov (United States)

    Henry, Gary T.; Fortner, C. Kevin; Thompson, Charles L.

    2010-01-01

    Evaluating the impacts of public school funding on student achievement has been an important objective for informing education policymaking but fraught with data and methodological limitations. Findings from prior research have been mixed at best, leaving policymakers with little advice about the benefits of allocating public resources to schools…

  18. Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

    Directory of Open Access Journals (Sweden)

    Yoonseok Shin

    2015-01-01

    Full Text Available Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

  19. Inverse estimation of multiple muscle activations based on linear logistic regression.

    Science.gov (United States)

    Sekiya, Masashi; Tsuji, Toshiaki

    2017-07-01

    This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model. The proposed method was compared with the LR model and ANN in the verification experiment with 7 participants. As a result, the proposed method showed better generalization performance than the conventional methods in various tasks.

  20. Relation of whole blood carboxyhemoglobin concentration to ambient carbon monoxide exposure estimated using regression.

    Science.gov (United States)

    Rudra, Carole B; Williams, Michelle A; Sheppard, Lianne; Koenig, Jane Q; Schiff, Melissa A; Frederick, Ihunnaya O; Dills, Russell

    2010-04-15

    Exposure to carbon monoxide (CO) and other ambient air pollutants is associated with adverse pregnancy outcomes. While there are several methods of estimating CO exposure, few have been evaluated against exposure biomarkers. The authors examined the relation between estimated CO exposure and blood carboxyhemoglobin concentration in 708 pregnant western Washington State women (1996-2004). Carboxyhemoglobin was measured in whole blood drawn around 13 weeks' gestation. CO exposure during the month of blood draw was estimated using a regression model containing predictor terms for year, month, street and population densities, and distance to the nearest major road. Year and month were the strongest predictors. Carboxyhemoglobin level was correlated with estimated CO exposure (rho = 0.22, 95% confidence interval (CI): 0.15, 0.29). After adjustment for covariates, each 10% increase in estimated exposure was associated with a 1.12% increase in median carboxyhemoglobin level (95% CI: 0.54, 1.69). This association remained after exclusion of 286 women who reported smoking or being exposed to secondhand smoke (rho = 0.24). In this subgroup, the median carboxyhemoglobin concentration increased 1.29% (95% CI: 0.67, 1.91) for each 10% increase in CO exposure. Monthly estimated CO exposure was moderately correlated with an exposure biomarker. These results support the validity of this regression model for estimating ambient CO exposures in this population and geographic setting.

  1. Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods

    Directory of Open Access Journals (Sweden)

    Yi-Ming Kuo

    2011-06-01

    Full Text Available Fine airborne particulate matter (PM2.5 has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS, the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME method. The resulting epistemic framework can assimilate knowledge bases including: (a empirical-based spatial trends of PM concentration based on landuse regression, (b the spatio-temporal dependence among PM observation information, and (c site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan from 2005–2007.

  2. Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods.

    Science.gov (United States)

    Yu, Hwa-Lung; Wang, Chih-Hsih; Liu, Ming-Che; Kuo, Yi-Ming

    2011-06-01

    Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan) from 2005-2007.

  3. Methods for estimating disease transmission rates: Evaluating the precision of Poisson regression and two novel methods

    DEFF Research Database (Denmark)

    Kirkeby, Carsten Thure; Hisham Beshara Halasa, Tariq; Gussmann, Maya Katrin

    2017-01-01

    the transmission rate. We use data from the two simulation models and vary the sampling intervals and the size of the population sampled. We devise two new methods to determine transmission rate, and compare these to the frequently used Poisson regression method in both epidemic and endemic situations. For most...... tested scenarios these new methods perform similar or better than Poisson regression, especially in the case of long sampling intervals. We conclude that transmission rate estimates are easily biased, which is important to take into account when using these rates in simulation models....

  4. A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction.

    Directory of Open Access Journals (Sweden)

    Hsin-Lun Wu

    Full Text Available Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT. Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2 required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006. Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001. Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.

  5. Graphical evaluation of the ridge-type robust regression estimators in mixture experiments.

    Science.gov (United States)

    Erkoc, Ali; Emiroglu, Esra; Akay, Kadri Ulas

    2014-01-01

    In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set.

  6. Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

    Science.gov (United States)

    Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

    2016-03-01

    From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

  7. Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression.

    Science.gov (United States)

    Zhen, Xiantong; Zhang, Heye; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo

    2017-02-01

    Cardiac four-chamber volume estimation serves as a fundamental and crucial role in clinical quantitative analysis of whole heart functions. It is a challenging task due to the huge complexity of the four chambers including great appearance variations, huge shape deformation and interference between chambers. Direct estimation has recently emerged as an effective and convenient tool for cardiac ventricular volume estimation. However, existing direct estimation methods were specifically developed for one single ventricle, i.e., left ventricle (LV), or bi-ventricles; they can not be directly used for four chamber volume estimation due to the great combinatorial variability and highly complex anatomical interdependency of the four chambers. In this paper, we propose a new, general framework for direct and simultaneous four chamber volume estimation. We have addressed two key issues, i.e., cardiac image representation and simultaneous four chamber volume estimation, which enables accurate and efficient four-chamber volume estimation. We generate compact and discriminative image representations by supervised descriptor learning (SDL) which can remove irrelevant information and extract discriminative features. We propose direct and simultaneous four-chamber volume estimation by the multioutput sparse latent regression (MSLR), which enables jointly modeling nonlinear input-output relationships and capturing four-chamber interdependence. The proposed method is highly generalized, independent of imaging modalities, which provides a general regression framework that can be extensively used for clinical data prediction to achieve automated diagnosis. Experiments on both MR and CT images show that our method achieves high performance with a correlation coefficient of up to 0.921 with ground truth obtained manually by human experts, which is clinically significant and enables more accurate, convenient and comprehensive assessment of cardiac functions. Copyright © 2016 Elsevier

  8. [Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

    Science.gov (United States)

    Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

    2016-03-01

    Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

  9. Mass estimation of loose parts in nuclear power plant based on multiple regression

    International Nuclear Information System (INIS)

    He, Yuanfeng; Cao, Yanlong; Yang, Jiangxin; Gan, Chunbiao

    2012-01-01

    According to the application of the Hilbert–Huang transform to the non-stationary signal and the relation between the mass of loose parts in nuclear power plant and corresponding frequency content, a new method for loose part mass estimation based on the marginal Hilbert–Huang spectrum (MHS) and multiple regression is proposed in this paper. The frequency spectrum of a loose part in a nuclear power plant can be expressed by the MHS. The multiple regression model that is constructed by the MHS feature of the impact signals for mass estimation is used to predict the unknown masses of a loose part. A simulated experiment verified that the method is feasible and the errors of the results are acceptable. (paper)

  10. Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography

    Directory of Open Access Journals (Sweden)

    Menon Carlo

    2011-09-01

    Full Text Available Abstract Background Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG signals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displacement, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training. Results It was shown that mean adjusted coefficient of determination (Ra2 values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean Ra2 values between 64% to 74% for different models. Conclusions Model estimation accuracy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estimation accuracy. Among the models compared, ordinary least squares linear regression model (OLS was shown to have high isometric torque estimation accuracy combined with very short training times.

  11. Development of flood regressions and climate change scenarios to explore estimates of future peak flows

    Science.gov (United States)

    Burns, Douglas A.; Smith, Martyn J.; Freehafer, Douglas A.

    2015-12-31

    A new Web-based application, titled “Application of Flood Regressions and Climate Change Scenarios To Explore Estimates of Future Peak Flows”, has been developed by the U.S. Geological Survey, in cooperation with the New York State Department of Transportation, that allows a user to apply a set of regression equations to estimate the magnitude of future floods for any stream or river in New York State (exclusive of Long Island) and the Lake Champlain Basin in Vermont. The regression equations that are the basis of the current application were developed in previous investigations by the U.S. Geological Survey (USGS) and are described at the USGS StreamStats Web sites for New York (http://water.usgs.gov/osw/streamstats/new_york.html) and Vermont (http://water.usgs.gov/osw/streamstats/Vermont.html). These regression equations include several fixed landscape metrics that quantify aspects of watershed geomorphology, basin size, and land cover as well as a climate variable—either annual precipitation or annual runoff.

  12. Estimation of Stature from Foot Dimensions and Stature among South Indian Medical Students Using Regression Models

    Directory of Open Access Journals (Sweden)

    Rajesh D. R

    2015-01-01

    Full Text Available Background: At times fragments of soft tissues are found disposed off in the open, in ditches at the crime scene and the same are brought to forensic experts for the purpose of identification and such type of cases pose a real challenge. Objectives: This study was aimed at developing a methodology which could help in personal identification by studying the relation between foot dimensions and stature among south subjects using regression models. Material and Methods: Stature and foot length of 100 subjects (age range 18-22 years were measured. Linear regression equations for stature estimation were calculated. Result: The correlation coefficients between stature and foot lengths were found to be positive and statistically significant. Height = 98.159 + 3.746 × FLRT (r = 0.821 and Height = 91.242 + 3.284 × FLRT (r = 0.837 are the regression formulas from foot lengths for males and females respectively. Conclusion: The regression equation derived in the study can be used reliably for estimation of stature in a diverse population group thus would be of immense value in the field of personal identification especially from mutilated bodies or fragmentary remains.

  13. In search of a corrected prescription drug elasticity estimate: a meta-regression approach.

    Science.gov (United States)

    Gemmill, Marin C; Costa-Font, Joan; McGuire, Alistair

    2007-06-01

    An understanding of the relationship between cost sharing and drug consumption depends on consistent and unbiased price elasticity estimates. However, there is wide heterogeneity among studies, which constrains the applicability of elasticity estimates for empirical purposes and policy simulation. This paper attempts to provide a corrected measure of the drug price elasticity by employing meta-regression analysis (MRA). The results indicate that the elasticity estimates are significantly different from zero, and the corrected elasticity is -0.209 when the results are made robust to heteroskedasticity and clustering of observations. Elasticity values are higher when the study was published in an economic journal, when the study employed a greater number of observations, and when the study used aggregate data. Elasticity estimates are lower when the institutional setting was a tax-based health insurance system.

  14. Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

    OpenAIRE

    Fang, Chuanglin; Liu, Haimeng; Li, Guangdong; Sun, Dongqi; Miao, Zhuang

    2015-01-01

    Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI) values at the city level, and employed Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Geographically Weighted Regression (GWR) to quantitatively estimate the comprehensive impact and spatial variati...

  15. Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution

    Directory of Open Access Journals (Sweden)

    Petrovic Nemanja

    2007-01-01

    Full Text Available We present a supervised learning-based approach for subpixel motion estimation which is then used to perform video super-resolution. The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework. The ranking formulation is a variant of classification and regression formulation, in which the ordering present in class labels namely, the shift between patches is explicitly taken into account. Finally, we demonstrate the applicability of our approach on superresolving synthetically generated images with global subpixel shifts and enhancing real video frames by accounting for both local integer and subpixel shifts.

  16. Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages

    Science.gov (United States)

    Kim, Yoonsang; Emery, Sherry

    2013-01-01

    Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods’ performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages—SAS GLIMMIX Laplace and SuperMix Gaussian quadrature—perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes. PMID:24288415

  17. Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

    Science.gov (United States)

    Kim, Yoonsang; Choi, Young-Ku; Emery, Sherry

    2013-08-01

    Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.

  18. Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.

    Science.gov (United States)

    Testa, S Marc; Winicki, Jessica M; Pearlson, Godfrey D; Gordon, Barry; Schretlen, David J

    2009-11-01

    Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.

  19. Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters

    Directory of Open Access Journals (Sweden)

    V. B. Goryainov

    2017-01-01

    Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the

  20. Regression to fuzziness method for estimation of remaining useful life in power plant components

    Science.gov (United States)

    Alamaniotis, Miltiadis; Grelle, Austin; Tsoukalas, Lefteri H.

    2014-10-01

    Mitigation of severe accidents in power plants requires the reliable operation of all systems and the on-time replacement of mechanical components. Therefore, the continuous surveillance of power systems is a crucial concern for the overall safety, cost control, and on-time maintenance of a power plant. In this paper a methodology called regression to fuzziness is presented that estimates the remaining useful life (RUL) of power plant components. The RUL is defined as the difference between the time that a measurement was taken and the estimated failure time of that component. The methodology aims to compensate for a potential lack of historical data by modeling an expert's operational experience and expertise applied to the system. It initially identifies critical degradation parameters and their associated value range. Once completed, the operator's experience is modeled through fuzzy sets which span the entire parameter range. This model is then synergistically used with linear regression and a component's failure point to estimate the RUL. The proposed methodology is tested on estimating the RUL of a turbine (the basic electrical generating component of a power plant) in three different cases. Results demonstrate the benefits of the methodology for components for which operational data is not readily available and emphasize the significance of the selection of fuzzy sets and the effect of knowledge representation on the predicted output. To verify the effectiveness of the methodology, it was benchmarked against the data-based simple linear regression model used for predictions which was shown to perform equal or worse than the presented methodology. Furthermore, methodology comparison highlighted the improvement in estimation offered by the adoption of appropriate of fuzzy sets for parameter representation.

  1. Testing and Estimating Shape-Constrained Nonparametric Density and Regression in the Presence of Measurement Error

    KAUST Repository

    Carroll, Raymond J.

    2011-03-01

    In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.

  2. Estimating HIES Data through Ratio and Regression Methods for Different Sampling Designs

    Directory of Open Access Journals (Sweden)

    Faqir Muhammad

    2007-01-01

    Full Text Available In this study, comparison has been made for different sampling designs, using the HIES data of North West Frontier Province (NWFP for 2001-02 and 1998-99 collected from the Federal Bureau of Statistics, Statistical Division, Government of Pakistan, Islamabad. The performance of the estimators has also been considered using bootstrap and Jacknife. A two-stage stratified random sample design is adopted by HIES. In the first stage, enumeration blocks and villages are treated as the first stage Primary Sampling Units (PSU. The sample PSU’s are selected with probability proportional to size. Secondary Sampling Units (SSU i.e., households are selected by systematic sampling with a random start. They have used a single study variable. We have compared the HIES technique with some other designs, which are: Stratified Simple Random Sampling. Stratified Systematic Sampling. Stratified Ranked Set Sampling. Stratified Two Phase Sampling. Ratio and Regression methods were applied with two study variables, which are: Income (y and Household sizes (x. Jacknife and Bootstrap are used for variance replication. Simple Random Sampling with sample size (462 to 561 gave moderate variances both by Jacknife and Bootstrap. By applying Systematic Sampling, we received moderate variance with sample size (467. In Jacknife with Systematic Sampling, we obtained variance of regression estimator greater than that of ratio estimator for a sample size (467 to 631. At a sample size (952 variance of ratio estimator gets greater than that of regression estimator. The most efficient design comes out to be Ranked set sampling compared with other designs. The Ranked set sampling with jackknife and bootstrap, gives minimum variance even with the smallest sample size (467. Two Phase sampling gave poor performance. Multi-stage sampling applied by HIES gave large variances especially if used with a single study variable.

  3. A robust background regression based score estimation algorithm for hyperspectral anomaly detection

    Science.gov (United States)

    Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

    2016-12-01

    Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement

  4. A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

    International Nuclear Information System (INIS)

    Baser, Furkan; Demirhan, Haydar

    2017-01-01

    Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics. - Highlights: • A fuzzy regression functions with support vector machines approach is proposed. • The approach is robust against outlier observations and over-fitting problem. • Estimation accuracy of the model is superior to several existent alternatives. • A new solar radiation estimation model is proposed for the region of Turkey. • The model is useful under complex terrestrial and climatic conditions.

  5. A different approach to estimate nonlinear regression model using numerical methods

    Science.gov (United States)

    Mahaboob, B.; Venkateswarlu, B.; Mokeshrayalu, G.; Balasiddamuni, P.

    2017-11-01

    This research paper concerns with the computational methods namely the Gauss-Newton method, Gradient algorithm methods (Newton-Raphson method, Steepest Descent or Steepest Ascent algorithm method, the Method of Scoring, the Method of Quadratic Hill-Climbing) based on numerical analysis to estimate parameters of nonlinear regression model in a very different way. Principles of matrix calculus have been used to discuss the Gradient-Algorithm methods. Yonathan Bard [1] discussed a comparison of gradient methods for the solution of nonlinear parameter estimation problems. However this article discusses an analytical approach to the gradient algorithm methods in a different way. This paper describes a new iterative technique namely Gauss-Newton method which differs from the iterative technique proposed by Gorden K. Smyth [2]. Hans Georg Bock et.al [10] proposed numerical methods for parameter estimation in DAE’s (Differential algebraic equation). Isabel Reis Dos Santos et al [11], Introduced weighted least squares procedure for estimating the unknown parameters of a nonlinear regression metamodel. For large-scale non smooth convex minimization the Hager and Zhang (HZ) conjugate gradient Method and the modified HZ (MHZ) method were presented by Gonglin Yuan et al [12].

  6. Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.

    Science.gov (United States)

    Wang, Wei; Griswold, Michael E

    2016-11-30

    The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  7. Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

    Science.gov (United States)

    Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

    2018-05-01

    Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

  8. A subagging regression method for estimating the qualitative and quantitative state of groundwater

    Science.gov (United States)

    Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young

    2017-08-01

    A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.

  9. Bias and efficiency loss in regression estimates due to duplicated observations: a Monte Carlo simulation

    Directory of Open Access Journals (Sweden)

    Francesco Sarracino

    2017-04-01

    Full Text Available Recent studies documented that survey data contain duplicate records. We assess how duplicate records affect regression estimates, and we evaluate the effectiveness of solutions to deal with duplicate records. Results show that the chances of obtaining unbiased estimates when data contain 40 doublets (about 5% of the sample range between 3.5% and 11.5% depending on the distribution of duplicates. If 7 quintuplets are present in the data (2% of the sample, then the probability of obtaining biased estimates ranges between 11% and 20%. Weighting the duplicate records by the inverse of their multiplicity, or dropping superfluous duplicates outperform other solutions in all considered scenarios. Our results illustrate the risk of using data in presence of duplicate records and call for further research on strategies to analyze affected data.

  10. Estimation of monthly solar exposure on horizontal surface by Angstrom-type regression equation

    International Nuclear Information System (INIS)

    Ravanshid, S.H.

    1981-01-01

    To obtain solar flux intensity, solar radiation measuring instruments are the best. In the absence of instrumental data there are other meteorological measurements which are related to solar energy and also it is possible to use empirical relationships to estimate solar flux intensit. One of these empirical relationships to estimate monthly averages of total solar radiation on a horizontal surface is the modified angstrom-type regression equation which has been employed in this report in order to estimate the solar flux intensity on a horizontal surface for Tehran. By comparing the results of this equation with four years measured valued by Tehran's meteorological weather station the values of meteorological constants (a,b) in the equation were obtained for Tehran. (author)

  11. Oil and gas pipeline construction cost analysis and developing regression models for cost estimation

    Science.gov (United States)

    Thaduri, Ravi Kiran

    In this study, cost data for 180 pipelines and 136 compressor stations have been analyzed. On the basis of the distribution analysis, regression models have been developed. Material, Labor, ROW and miscellaneous costs make up the total cost of a pipeline construction. The pipelines are analyzed based on different pipeline lengths, diameter, location, pipeline volume and year of completion. In a pipeline construction, labor costs dominate the total costs with a share of about 40%. Multiple non-linear regression models are developed to estimate the component costs of pipelines for various cross-sectional areas, lengths and locations. The Compressor stations are analyzed based on the capacity, year of completion and location. Unlike the pipeline costs, material costs dominate the total costs in the construction of compressor station, with an average share of about 50.6%. Land costs have very little influence on the total costs. Similar regression models are developed to estimate the component costs of compressor station for various capacities and locations.

  12. A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

    Science.gov (United States)

    Saunders, Christina T; Blume, Jeffrey D

    2017-10-26

    Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

  13. Mathematical models for estimating earthquake casualties and damage cost through regression analysis using matrices

    International Nuclear Information System (INIS)

    Urrutia, J D; Bautista, L A; Baccay, E B

    2014-01-01

    The aim of this study was to develop mathematical models for estimating earthquake casualties such as death, number of injured persons, affected families and total cost of damage. To quantify the direct damages from earthquakes to human beings and properties given the magnitude, intensity, depth of focus, location of epicentre and time duration, the regression models were made. The researchers formulated models through regression analysis using matrices and used α = 0.01. The study considered thirty destructive earthquakes that hit the Philippines from the inclusive years 1968 to 2012. Relevant data about these said earthquakes were obtained from Philippine Institute of Volcanology and Seismology. Data on damages and casualties were gathered from the records of National Disaster Risk Reduction and Management Council. This study will be of great value in emergency planning, initiating and updating programs for earthquake hazard reduction in the Philippines, which is an earthquake-prone country.

  14. Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model

    Science.gov (United States)

    Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei

    2014-10-01

    Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.

  15. An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

    Science.gov (United States)

    Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

    2018-05-01

    Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

  16. Large biases in regression-based constituent flux estimates: causes and diagnostic tools

    Science.gov (United States)

    Hirsch, Robert M.

    2014-01-01

    It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

  17. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems

    Directory of Open Access Journals (Sweden)

    Hongjian Wang

    2014-01-01

    Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.

  18. Estimating Gestational Age With Sonography: Regression-Derived Formula Versus the Fetal Biometric Average.

    Science.gov (United States)

    Cawyer, Chase R; Anderson, Sarah B; Szychowski, Jeff M; Neely, Cherry; Owen, John

    2018-03-01

    To compare the accuracy of a new regression-derived formula developed from the National Fetal Growth Studies data to the common alternative method that uses the average of the gestational ages (GAs) calculated for each fetal biometric measurement (biparietal diameter, head circumference, abdominal circumference, and femur length). This retrospective cross-sectional study identified nonanomalous singleton pregnancies that had a crown-rump length plus at least 1 additional sonographic examination with complete fetal biometric measurements. With the use of the crown-rump length to establish the referent estimated date of delivery, each method's (National Institute of Child Health and Human Development regression versus Hadlock average [Radiology 1984; 152:497-501]), error at every examination was computed. Error, defined as the difference between the crown-rump length-derived GA and each method's predicted GA (weeks), was compared in 3 GA intervals: 1 (14 weeks-20 weeks 6 days), 2 (21 weeks-28 weeks 6 days), and 3 (≥29 weeks). In addition, the proportion of each method's examinations that had errors outside prespecified (±) day ranges was computed by using odds ratios. A total of 16,904 sonograms were identified. The overall and prespecified GA range subset mean errors were significantly smaller for the regression compared to the average (P < .01), and the regression had significantly lower odds of observing examinations outside the specified range of error in GA intervals 2 (odds ratio, 1.15; 95% confidence interval, 1.01-1.31) and 3 (odds ratio, 1.24; 95% confidence interval, 1.17-1.32) than the average method. In a contemporary unselected population of women dated by a crown-rump length-derived GA, the National Institute of Child Health and Human Development regression formula produced fewer estimates outside a prespecified margin of error than the commonly used Hadlock average; the differences were most pronounced for GA estimates at 29 weeks and later.

  19. Impact of regression methods on improved effects of soil structure on soil water retention estimates

    Science.gov (United States)

    Nguyen, Phuong Minh; De Pue, Jan; Le, Khoa Van; Cornelis, Wim

    2015-06-01

    Increasing the accuracy of pedotransfer functions (PTFs), an indirect method for predicting non-readily available soil features such as soil water retention characteristics (SWRC), is of crucial importance for large scale agro-hydrological modeling. Adding significant predictors (i.e., soil structure), and implementing more flexible regression algorithms are among the main strategies of PTFs improvement. The aim of this study was to investigate whether the improved effect of categorical soil structure information on estimating soil-water content at various matric potentials, which has been reported in literature, could be enduringly captured by regression techniques other than the usually applied linear regression. Two data mining techniques, i.e., Support Vector Machines (SVM), and k-Nearest Neighbors (kNN), which have been recently introduced as promising tools for PTF development, were utilized to test if the incorporation of soil structure will improve PTF's accuracy under a context of rather limited training data. The results show that incorporating descriptive soil structure information, i.e., massive, structured and structureless, as grouping criterion can improve the accuracy of PTFs derived by SVM approach in the range of matric potential of -6 to -33 kPa (average RMSE decreased up to 0.005 m3 m-3 after grouping, depending on matric potentials). The improvement was primarily attributed to the outperformance of SVM-PTFs calibrated on structureless soils. No improvement was obtained with kNN technique, at least not in our study in which the data set became limited in size after grouping. Since there is an impact of regression techniques on the improved effect of incorporating qualitative soil structure information, selecting a proper technique will help to maximize the combined influence of flexible regression algorithms and soil structure information on PTF accuracy.

  20. Development and Application of Watershed Regressions for Pesticides (WARP) for Estimating Atrazine Concentration Distributions in Streams

    Science.gov (United States)

    Larson, Steven J.; Crawford, Charles G.; Gilliom, Robert J.

    2004-01-01

    Regression models were developed for predicting atrazine concentration distributions in rivers and streams, using the Watershed Regressions for Pesticides (WARP) methodology. Separate regression equations were derived for each of nine percentiles of the annual distribution of atrazine concentrations and for the annual time-weighted mean atrazine concentration. In addition, seasonal models were developed for two specific periods of the year--the high season, when the highest atrazine concentrations are expected in streams, and the low season, when concentrations are expected to be low or undetectable. Various nationally available watershed parameters were used as explanatory variables, including atrazine use intensity, soil characteristics, hydrologic parameters, climate and weather variables, land use, and agricultural management practices. Concentration data from 112 river and stream stations sampled as part of the U.S. Geological Survey's National Water-Quality Assessment and National Stream Quality Accounting Network Programs were used for computing the concentration percentiles and mean concentrations used as the response variables in regression models. Tobit regression methods, using maximum likelihood estimation, were used for developing the models because some of the concentration values used for the response variables were censored (reported as less than a detection threshold). Data from 26 stations not used for model development were used for model validation. The annual models accounted for 62 to 77 percent of the variability in concentrations among the 112 model development stations. Atrazine use intensity (the amount of atrazine used in the watershed divided by watershed area) was the most important explanatory variable in all models, but additional watershed parameters significantly increased the amount of variability explained by the models. Predicted concentrations from all 10 models were within a factor of 10 of the observed concentrations at most

  1. Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Morufu Olusola Ibitoye

    2016-07-01

    Full Text Available The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70% and testing (30% subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R2 between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.

  2. The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

    DEFF Research Database (Denmark)

    Brunori, Paolo; Hufe, Paul; Mahler, Daniel Gerszon

    2017-01-01

    the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make...... the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions....

  3. Estimation of a Reactor Core Power Peaking Factor Using Support Vector Regression and Uncertainty Analysis

    International Nuclear Information System (INIS)

    Bae, In Ho; Naa, Man Gyun; Lee, Yoon Joon; Park, Goon Cherl

    2009-01-01

    The monitoring of detailed 3-dimensional (3D) reactor core power distribution is a prerequisite in the operation of nuclear power reactors to ensure that various safety limits imposed on the LPD and DNBR, are not violated during nuclear power reactor operation. The LPD and DNBR should be calculated in order to perform the two major functions of the core protection calculator system (CPCS) and the core operation limit supervisory system (COLSS). The LPD at the hottest part of a hot fuel rod, which is related to the power peaking factor (PPF, F q ), is more important than the LPD at any other position in a reactor core. The LPD needs to be estimated accurately to prevent nuclear fuel rods from melting. In this study, support vector regression (SVR) and uncertainty analysis have been applied to estimation of reactor core power peaking factor

  4. Soil moisture estimation using multi linear regression with terraSAR-X data

    Directory of Open Access Journals (Sweden)

    G. García

    2016-06-01

    Full Text Available The first five centimeters of soil form an interface where the main heat fluxes exchanges between the land surface and the atmosphere occur. Besides ground measurements, remote sensing has proven to be an excellent tool for the monitoring of spatial and temporal distributed data of the most relevant Earth surface parameters including soil’s parameters. Indeed, active microwave sensors (Synthetic Aperture Radar - SAR offer the opportunity to monitor soil moisture (HS at global, regional and local scales by monitoring involved processes. Several inversion algorithms, that derive geophysical information as HS from SAR data, were developed. Many of them use electromagnetic models for simulating the backscattering coefficient and are based on statistical techniques, such as neural networks, inversion methods and regression models. Recent studies have shown that simple multiple regression techniques yield satisfactory results. The involved geophysical variables in these methodologies are descriptive of the soil structure, microwave characteristics and land use. Therefore, in this paper we aim at developing a multiple linear regression model to estimate HS on flat agricultural regions using TerraSAR-X satellite data and data from a ground weather station. The results show that the backscatter, the precipitation and the relative humidity are the explanatory variables of HS. The results obtained presented a RMSE of 5.4 and a R2  of about 0.6

  5. Influence of regression model and initial intensity of an incremental test on the relationship between the lactate threshold estimated by the maximal-deviation method and running performance.

    Science.gov (United States)

    Santos-Concejero, Jordan; Tucker, Ross; Granados, Cristina; Irazusta, Jon; Bidaurrazaga-Letona, Iraia; Zabala-Lili, Jon; Gil, Susana María

    2014-01-01

    This study investigated the influence of the regression model and initial intensity during an incremental test on the relationship between the lactate threshold estimated by the maximal-deviation method and performance in elite-standard runners. Twenty-three well-trained runners completed a discontinuous incremental running test on a treadmill. Speed started at 9 km · h(-1) and increased by 1.5 km · h(-1) every 4 min until exhaustion, with a minute of recovery for blood collection. Lactate-speed data were fitted by exponential and polynomial models. The lactate threshold was determined for both models, using all the co-ordinates, excluding the first and excluding the first and second points. The exponential lactate threshold was greater than the polynomial equivalent in any co-ordinate condition (P performance and is independent of the initial intensity of the test.

  6. Estimation of genetic parameters related to eggshell strength using random regression models.

    Science.gov (United States)

    Guo, J; Ma, M; Qu, L; Shen, M; Dou, T; Wang, K

    2015-01-01

    This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen's laying life using random regression. The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens. Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week. The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials. The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67. The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues. The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

  7. Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

    Directory of Open Access Journals (Sweden)

    Chuanglin Fang

    2015-11-01

    Full Text Available Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI values at the city level, and employed Ordinary Least Squares (OLS, Spatial Lag Model (SAR, and Geographically Weighted Regression (GWR to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.

  8. Support vector regression methodology for estimating global solar radiation in Algeria

    Science.gov (United States)

    Guermoui, Mawloud; Rabehi, Abdelaziz; Gairaa, Kacem; Benkaciali, Said

    2018-01-01

    Accurate estimation of Daily Global Solar Radiation (DGSR) has been a major goal for solar energy applications. In this paper we show the possibility of developing a simple model based on the Support Vector Regression (SVM-R), which could be used to estimate DGSR on the horizontal surface in Algeria based only on sunshine ratio as input. The SVM model has been developed and tested using a data set recorded over three years (2005-2007). The data was collected at the Applied Research Unit for Renewable Energies (URAER) in Ghardaïa city. The data collected between 2005-2006 are used to train the model while the 2007 data are used to test the performance of the selected model. The measured and the estimated values of DGSR were compared during the testing phase statistically using the Root Mean Square Error (RMSE), Relative Square Error (rRMSE), and correlation coefficient (r2), which amount to 1.59(MJ/m2), 8.46 and 97,4%, respectively. The obtained results show that the SVM-R is highly qualified for DGSR estimation using only sunshine ratio.

  9. Flexible regression models for estimating postmortem interval (PMI) in forensic medicine.

    Science.gov (United States)

    Muñoz Barús, José Ignacio; Febrero-Bande, Manuel; Cadarso-Suárez, Carmen

    2008-10-30

    Correct determination of time of death is an important goal in forensic medicine. Numerous methods have been described for estimating postmortem interval (PMI), but most are imprecise, poorly reproducible and/or have not been validated with real data. In recent years, however, some progress in PMI estimation has been made, notably through the use of new biochemical methods for quantifying relevant indicator compounds in the vitreous humour. The best, but unverified, results have been obtained with [K+] and hypoxanthine [Hx], using simple linear regression (LR) models. The main aim of this paper is to offer more flexible alternatives to LR, such as generalized additive models (GAMs) and support vector machines (SVMs) in order to obtain improved PMI estimates. The present study, based on detailed analysis of [K+] and [Hx] in more than 200 vitreous humour samples from subjects with known PMI, compared classical LR methodology with GAM and SVM methodologies. Both proved better than LR for estimation of PMI. SVM showed somewhat greater precision than GAM, but GAM offers a readily interpretable graphical output, facilitating understanding of findings by legal professionals; there are thus arguments for using both types of models. R code for these methods is available from the authors, permitting accurate prediction of PMI from vitreous humour [K+], [Hx] and [U], with confidence intervals and graphical output provided. Copyright 2008 John Wiley & Sons, Ltd.

  10. Evaluation of Regression and Neuro_Fuzzy Models in Estimating Saturated Hydraulic Conductivity

    Directory of Open Access Journals (Sweden)

    J. Behmanesh

    2015-06-01

    Full Text Available Study of soil hydraulic properties such as saturated and unsaturated hydraulic conductivity is required in the environmental investigations. Despite numerous research, measuring saturated hydraulic conductivity using by direct methods are still costly, time consuming and professional. Therefore estimating saturated hydraulic conductivity using rapid and low cost methods such as pedo-transfer functions with acceptable accuracy was developed. The purpose of this research was to compare and evaluate 11 pedo-transfer functions and Adaptive Neuro-Fuzzy Inference System (ANFIS to estimate saturated hydraulic conductivity of soil. In this direct, saturated hydraulic conductivity and physical properties in 40 points of Urmia were calculated. The soil excavated was used in the lab to determine its easily accessible parameters. The results showed that among existing models, Aimrun et al model had the best estimation for soil saturated hydraulic conductivity. For mentioned model, the Root Mean Square Error and Mean Absolute Error parameters were 0.174 and 0.028 m/day respectively. The results of the present research, emphasises the importance of effective porosity application as an important accessible parameter in accuracy of pedo-transfer functions. sand and silt percent, bulk density and soil particle density were selected to apply in 561 ANFIS models. In training phase of best ANFIS model, the R2 and RMSE were calculated 1 and 1.2×10-7 respectively. These amounts in the test phase were 0.98 and 0.0006 respectively. Comparison of regression and ANFIS models showed that the ANFIS model had better results than regression functions. Also Nuro-Fuzzy Inference System had capability to estimatae with high accuracy in various soil textures.

  11. Estimating Penetration Resistance in Agricultural Soils of Ardabil Plain Using Artificial Neural Network and Regression Methods

    Directory of Open Access Journals (Sweden)

    Gholam Reza Sheykhzadeh

    2017-02-01

    Full Text Available Introduction: Penetration resistance is one of the criteria for evaluating soil compaction. It correlates with several soil properties such as vehicle trafficability, resistance to root penetration, seedling emergence, and soil compaction by farm machinery. Direct measurement of penetration resistance is time consuming and difficult because of high temporal and spatial variability. Therefore, many different regressions and artificial neural network pedotransfer functions have been proposed to estimate penetration resistance from readily available soil variables such as particle size distribution, bulk density (Db and gravimetric water content (θm. The lands of Ardabil Province are one of the main production regions of potato in Iran, thus, obtaining the soil penetration resistance in these regions help with the management of potato production. The objective of this research was to derive pedotransfer functions by using regression and artificial neural network to predict penetration resistance from some soil variations in the agricultural soils of Ardabil plain and to compare the performance of artificial neural network with regression models. Materials and methods: Disturbed and undisturbed soil samples (n= 105 were systematically taken from 0-10 cm soil depth with nearly 3000 m distance in the agricultural lands of the Ardabil plain ((lat 38°15' to 38°40' N, long 48°16' to 48°61' E. The contents of sand, silt and clay (hydrometer method, CaCO3 (titration method, bulk density (cylinder method, particle density (Dp (pychnometer method, organic carbon (wet oxidation method, total porosity(calculating from Db and Dp, saturated (θs and field soil water (θf using the gravimetric method were measured in the laboratory. Mean geometric diameter (dg and standard deviation (σg of soil particles were computed using the percentages of sand, silt and clay. Penetration resistance was measured in situ using cone penetrometer (analog model at 10

  12. A regressive methodology for estimating missing data in rainfall daily time series

    Science.gov (United States)

    Barca, E.; Passarella, G.

    2009-04-01

    The "presence" of gaps in environmental data time series represents a very common, but extremely critical problem, since it can produce biased results (Rubin, 1976). Missing data plagues almost all surveys. The problem is how to deal with missing data once it has been deemed impossible to recover the actual missing values. Apart from the amount of missing data, another issue which plays an important role in the choice of any recovery approach is the evaluation of "missingness" mechanisms. When data missing is conditioned by some other variable observed in the data set (Schafer, 1997) the mechanism is called MAR (Missing at Random). Otherwise, when the missingness mechanism depends on the actual value of the missing data, it is called NCAR (Not Missing at Random). This last is the most difficult condition to model. In the last decade interest arose in the estimation of missing data by using regression (single imputation). More recently multiple imputation has become also available, which returns a distribution of estimated values (Scheffer, 2002). In this paper an automatic methodology for estimating missing data is presented. In practice, given a gauging station affected by missing data (target station), the methodology checks the randomness of the missing data and classifies the "similarity" between the target station and the other gauging stations spread over the study area. Among different methods useful for defining the similarity degree, whose effectiveness strongly depends on the data distribution, the Spearman correlation coefficient was chosen. Once defined the similarity matrix, a suitable, nonparametric, univariate, and regressive method was applied in order to estimate missing data in the target station: the Theil method (Theil, 1950). Even though the methodology revealed to be rather reliable an improvement of the missing data estimation can be achieved by a generalization. A first possible improvement consists in extending the univariate technique to

  13. Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong.

    Science.gov (United States)

    Tang, Robert; Tian, Linwei; Thach, Thuan-Quoc; Tsui, Tsz Him; Brauer, Michael; Lee, Martha; Allen, Ryan; Yuchi, Weiran; Lai, Poh-Chin; Wong, Paulina; Barratt, Benjamin

    2018-04-01

    Epidemiological studies typically use subjects' residential address to estimate individuals' air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks. To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong. A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component. Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM 2.5 , BC, and NO 2 were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses. The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than 'true' exposures. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Improved regression models for ventilation estimation based on chest and abdomen movements

    International Nuclear Information System (INIS)

    Liu, Shaopeng; Gao, Robert; He, Qingbo; Staudenmayer, John; Freedson, Patty

    2012-01-01

    Non-invasive estimation of minute ventilation is important for quantifying the intensity of physical activity of individuals. In this paper, several improved regression models are presented, based on the measurement of chest and abdomen movements from sensor belts worn by subjects (n = 50) engaged in 14 types of physical activity. Five linear models involving a combination of 11 features were developed, and the effects of different model training approaches and window sizes for computing the features were investigated. The performance of the models was evaluated using experimental data collected during the physical activity protocol. The predicted minute ventilation was compared to the criterion ventilation measured using a bidirectional digital volume transducer housed in a respiratory gas exchange system. The results indicate that the inclusion of breathing frequency and the use of percentile points instead of interdecile ranges over a 60 s window size reduced error by about 43%, when applied to the classical two-degrees-of-freedom model. The mean percentage error of the minute ventilation estimated for all the activities was below 7.5%, verifying reasonably good performance of the models and the applicability of the wearable sensing system for minute ventilation estimation during physical activity. (paper)

  15. Dual Regression

    OpenAIRE

    Spady, Richard; Stouli, Sami

    2012-01-01

    We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

  16. A modified approach to estimating sample size for simple logistic regression with one continuous covariate.

    Science.gov (United States)

    Novikov, I; Fund, N; Freedman, L S

    2010-01-15

    Different methods for the calculation of sample size for simple logistic regression (LR) with one normally distributed continuous covariate give different results. Sometimes the difference can be large. Furthermore, some methods require the user to specify the prevalence of cases when the covariate equals its population mean, rather than the more natural population prevalence. We focus on two commonly used methods and show through simulations that the power for a given sample size may differ substantially from the nominal value for one method, especially when the covariate effect is large, while the other method performs poorly if the user provides the population prevalence instead of the required parameter. We propose a modification of the method of Hsieh et al. that requires specification of the population prevalence and that employs Schouten's sample size formula for a t-test with unequal variances and group sizes. This approach appears to increase the accuracy of the sample size estimates for LR with one continuous covariate.

  17. A Note on Penalized Regression Spline Estimation in the Secondary Analysis of Case-Control Data

    KAUST Repository

    Gazioglu, Suzan; Wei, Jiawei; Jennings, Elizabeth M.; Carroll, Raymond J.

    2013-01-01

    Primary analysis of case-control studies focuses on the relationship between disease (D) and a set of covariates of interest (Y, X). A secondary application of the case-control study, often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated due to the case-control sampling, and to avoid the biased sampling that arises from the design, it is typical to use the control data only. In this paper, we develop penalized regression spline methodology that uses all the data, and improves precision of estimation compared to using only the controls. A simulation study and an empirical example are used to illustrate the methodology.

  18. A Note on Penalized Regression Spline Estimation in the Secondary Analysis of Case-Control Data

    KAUST Repository

    Gazioglu, Suzan

    2013-05-25

    Primary analysis of case-control studies focuses on the relationship between disease (D) and a set of covariates of interest (Y, X). A secondary application of the case-control study, often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated due to the case-control sampling, and to avoid the biased sampling that arises from the design, it is typical to use the control data only. In this paper, we develop penalized regression spline methodology that uses all the data, and improves precision of estimation compared to using only the controls. A simulation study and an empirical example are used to illustrate the methodology.

  19. Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.

    Science.gov (United States)

    Messier, Kyle P; Akita, Yasuyuki; Serre, Marc L

    2012-03-06

    Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

  20. Multinomial Logistic Regression & Bootstrapping for Bayesian Estimation of Vertical Facies Prediction in Heterogeneous Sandstone Reservoirs

    Science.gov (United States)

    Al-Mudhafar, W. J.

    2013-12-01

    Precisely prediction of rock facies leads to adequate reservoir characterization by improving the porosity-permeability relationships to estimate the properties in non-cored intervals. It also helps to accurately identify the spatial facies distribution to perform an accurate reservoir model for optimal future reservoir performance. In this paper, the facies estimation has been done through Multinomial logistic regression (MLR) with respect to the well logs and core data in a well in upper sandstone formation of South Rumaila oil field. The entire independent variables are gamma rays, formation density, water saturation, shale volume, log porosity, core porosity, and core permeability. Firstly, Robust Sequential Imputation Algorithm has been considered to impute the missing data. This algorithm starts from a complete subset of the dataset and estimates sequentially the missing values in an incomplete observation by minimizing the determinant of the covariance of the augmented data matrix. Then, the observation is added to the complete data matrix and the algorithm continues with the next observation with missing values. The MLR has been chosen to estimate the maximum likelihood and minimize the standard error for the nonlinear relationships between facies & core and log data. The MLR is used to predict the probabilities of the different possible facies given each independent variable by constructing a linear predictor function having a set of weights that are linearly combined with the independent variables by using a dot product. Beta distribution of facies has been considered as prior knowledge and the resulted predicted probability (posterior) has been estimated from MLR based on Baye's theorem that represents the relationship between predicted probability (posterior) with the conditional probability and the prior knowledge. To assess the statistical accuracy of the model, the bootstrap should be carried out to estimate extra-sample prediction error by randomly

  1. Monopole and dipole estimation for multi-frequency sky maps by linear regression

    Science.gov (United States)

    Wehus, I. K.; Fuskeland, U.; Eriksen, H. K.; Banday, A. J.; Dickinson, C.; Ghosh, T.; Górski, K. M.; Lawrence, C. R.; Leahy, J. P.; Maino, D.; Reich, P.; Reich, W.

    2017-01-01

    We describe a simple but efficient method for deriving a consistent set of monopole and dipole corrections for multi-frequency sky map data sets, allowing robust parametric component separation with the same data set. The computational core of this method is linear regression between pairs of frequency maps, often called T-T plots. Individual contributions from monopole and dipole terms are determined by performing the regression locally in patches on the sky, while the degeneracy between different frequencies is lifted whenever the dominant foreground component exhibits a significant spatial spectral index variation. Based on this method, we present two different, but each internally consistent, sets of monopole and dipole coefficients for the nine-year WMAP, Planck 2013, SFD 100 μm, Haslam 408 MHz and Reich & Reich 1420 MHz maps. The two sets have been derived with different analysis assumptions and data selection, and provide an estimate of residual systematic uncertainties. In general, our values are in good agreement with previously published results. Among the most notable results are a relative dipole between the WMAP and Planck experiments of 10-15μK (depending on frequency), an estimate of the 408 MHz map monopole of 8.9 ± 1.3 K, and a non-zero dipole in the 1420 MHz map of 0.15 ± 0.03 K pointing towards Galactic coordinates (l,b) = (308°,-36°) ± 14°. These values represent the sum of any instrumental and data processing offsets, as well as any Galactic or extra-Galactic component that is spectrally uniform over the full sky.

  2. An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models

    International Nuclear Information System (INIS)

    Harlim, John; Mahdi, Adam; Majda, Andrew J.

    2014-01-01

    A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model

  3. Estimation of the laser cutting operating cost by support vector regression methodology

    Science.gov (United States)

    Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam

    2016-09-01

    Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.

  4. Estimating carbon and showing impacts of drought using satellite data in regression-tree models

    Science.gov (United States)

    Boyte, Stephen; Wylie, Bruce K.; Howard, Danny; Dahal, Devendra; Gilmanov, Tagir G.

    2018-01-01

    Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.

  5. Regression methodology in groundwater composition estimation with composition predictions for Romuvaara borehole KR10

    Energy Technology Data Exchange (ETDEWEB)

    Luukkonen, A.; Korkealaakso, J.; Pitkaenen, P. [VTT Communities and Infrastructure, Espoo (Finland)

    1997-11-01

    Teollisuuden Voima Oy selected five investigation areas for preliminary site studies (1987Ae1992). The more detailed site investigation project, launched at the beginning of 1993 and presently supervised by Posiva Oy, is concentrated to three investigation areas. Romuvaara at Kuhmo is one of the present target areas, and the geochemical, structural and hydrological data used in this study are extracted from there. The aim of the study is to develop suitable methods for groundwater composition estimation based on a group of known hydrogeological variables. The input variables used are related to the host type of groundwater, hydrological conditions around the host location, mixing potentials between different types of groundwater, and minerals equilibrated with the groundwater. The output variables are electrical conductivity, Ca, Mg, Mn, Na, K, Fe, Cl, S, HS, SO{sub 4}, alkalinity, {sup 3}H, {sup 14}C, {sup 13}C, Al, Sr, F, Br and I concentrations, and pH of the groundwater. The methodology is to associate the known hydrogeological conditions (i.e. input variables), with the known water compositions (output variables), and to evaluate mathematical relations between these groups. Output estimations are done with two separate procedures: partial least squares regressions on the principal components of input variables, and by training neural networks with input-output pairs. Coefficients of linear equations and trained networks are optional methods for actual predictions. The quality of output predictions are monitored with confidence limit estimations, evaluated from input variable covariances and output variances, and with charge balance calculations. Groundwater compositions in Romuvaara borehole KR10 are predicted at 10 metre intervals with both prediction methods. 46 refs.

  6. Multiple linear regression to estimate time-frequency electrophysiological responses in single trials.

    Science.gov (United States)

    Hu, L; Zhang, Z G; Mouraux, A; Iannetti, G D

    2015-05-01

    Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical

  7. Estimating Engineering and Manufacturing Development Cost Risk Using Logistic and Multiple Regression

    National Research Council Canada - National Science Library

    Bielecki, John

    2003-01-01

    .... Previous research has demonstrated the use of a two-step logistic and multiple regression methodology to predicting cost growth produces desirable results versus traditional single-step regression...

  8. Outlier Detection in Regression Using an Iterated One-Step Approximation to the Huber-Skip Estimator

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Bent

    2013-01-01

    In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber-skip estima......In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber...... that the normalized estimation errors are tight and are close to a linear function of the kernel, thus providing a stochastic expansion of the estimators, which is the same as for the Huber-skip. This implies that the iterated estimator is a close approximation of the Huber-skip...

  9. The Growth Points of Regional Economy and Regression Estimation for Branch Investment Multipliers

    Directory of Open Access Journals (Sweden)

    Nina Pavlovna Goridko

    2018-03-01

    Full Text Available The article develops the methodology of using investment multipliers to identify growth points for a regional economy. The paper discusses various options for the assessment of multiplicative effects caused by investments in certain sectors of the economy. All calculations are carried out on the example of economy of the Republic of Tatarstan for the period 2005–2015. The instrument of regression modeling using the method of least squares, permits to estimate sectoral and cross-sectoral investment multipliers in the economy of the Republic of Tatarstan. Moreover, this method allows to assess the elasticity of gross output of regional economy and its individual sectors depending on investment in various sectors of the economy. Calculations results allowed to identify three growth points of the economy of the Republic of Tatarstan. They are mining industry, manufacturing industry and construction. The success of a particular industry or sub-industry in a country or a region should be measured not only by its share in macro-system’s gross output or value added, but also by the multiplicative effect that investments in the industry have on the development of other industries, on employment and on general national or regional product. In recent years, the growth of the Russian was close to zero. Thus, it is crucial to understand the structural consequences of the increasing investments in various sectors of the Russian economy. In this regard, the problems solved in the article are relevant for a number of countries and regions with a similar economic situation. The obtained results can be applied for similar estimations of investment multipliers as well as multipliers of government spending, and other components of aggregate demand in various countries and regions to identify growth points. Investments in these growth points will induce the greatest and the most evident increment of the outcome from the macro-system’s economic activities.

  10. Estimation of Chinese surface NO2 concentrations combining satellite data and Land Use Regression

    Science.gov (United States)

    Anand, J.; Monks, P.

    2016-12-01

    Monitoring surface-level air quality is often limited by in-situ instrument placement and issues arising from harmonisation over long timescales. Satellite instruments can offer a synoptic view of regional pollution sources, but in many cases only a total or tropospheric column can be measured. In this work a new technique of estimating surface NO2 combining both satellite and in-situ data is presented, in which a Land Use Regression (LUR) model is used to create high resolution pollution maps based on known predictor variables such as population density, road networks, and land cover. By employing a mixed effects approach, it is possible to take advantage of the spatiotemporal variability in the satellite-derived column densities to account for daily and regional variations in surface NO2 caused by factors such as temperature, elevation, and wind advection. In this work, surface NO2 maps are modelled over the North China Plain and Pearl River Delta during high-pollution episodes by combining in-situ measurements and tropospheric columns from the Ozone Monitoring Instrument (OMI). The modelled concentrations show good agreement with in-situ data and surface NO2 concentrations derived from the MACC-II global reanalysis.

  11. Estimated prevalence of halitosis: a systematic review and meta-regression analysis.

    Science.gov (United States)

    Silva, Manuela F; Leite, Fábio R M; Ferreira, Larissa B; Pola, Natália M; Scannapieco, Frank A; Demarco, Flávio F; Nascimento, Gustavo G

    2018-01-01

    This study aims to conduct a systematic review to determine the prevalence of halitosis in adolescents and adults. Electronic searches were performed using four different databases without restrictions: PubMed, Scopus, Web of Science, and SciELO. Population-based observational studies that provided data about the prevalence of halitosis in adolescents and adults were included. Additionally, meta-analyses, meta-regression, and sensitivity analyses were conducted to synthesize the evidence. A total of 584 articles were initially found and considered for title and abstract evaluation. Thirteen articles met inclusion criteria. The combined prevalence of halitosis was found to be 31.8% (95% CI 24.6-39.0%). Methodological aspects such as the year of publication and the socioeconomic status of the country where the study was conducted seemed to influence the prevalence of halitosis. Our results demonstrated that the estimated prevalence of halitosis was 31.8%, with high heterogeneity between studies. The results suggest a worldwide trend towards a rise in halitosis prevalence. Given the high prevalence of halitosis and its complex etiology, dental professionals should be aware of their roles in halitosis prevention and treatment.

  12. ESTIMATION OF GENETIC PARAMETERS IN TROPICARNE CATTLE WITH RANDOM REGRESSION MODELS USING B-SPLINES

    Directory of Open Access Journals (Sweden)

    Joel Domínguez Viveros

    2015-04-01

    Full Text Available The objectives were to estimate variance components, and direct (h2 and maternal (m2 heritability in the growth of Tropicarne cattle based on a random regression model using B-Splines for random effects modeling. Information from 12 890 monthly weightings of 1787 calves, from birth to 24 months old, was analyzed. The pedigree included 2504 animals. The random effects model included genetic and permanent environmental (direct and maternal of cubic order, and residuals. The fixed effects included contemporaneous groups (year – season of weighed, sex and the covariate age of the cow (linear and quadratic. The B-Splines were defined in four knots through the growth period analyzed. Analyses were performed with the software Wombat. The variances (phenotypic and residual presented a similar behavior; of 7 to 12 months of age had a negative trend; from birth to 6 months and 13 to 18 months had positive trend; after 19 months were maintained constant. The m2 were low and near to zero, with an average of 0.06 in an interval of 0.04 to 0.11; the h2 also were close to zero, with an average of 0.10 in an interval of 0.03 to 0.23.

  13. Estimation of trabecular bone parameters in children from multisequence MRI using texture-based regression

    Energy Technology Data Exchange (ETDEWEB)

    Lekadir, Karim, E-mail: karim.lekadir@upf.edu; Hoogendoorn, Corné [Center for Computational Imaging and Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona 08018 (Spain); Armitage, Paul [The Academic Unit of Radiology, The University of Sheffield, Sheffield S10 2JF (United Kingdom); Whitby, Elspeth [The Academic Unit of Reproductive and Developmental Medicine, The University of Sheffield, Sheffield S10 2SF (United Kingdom); King, David [The Academic Unit of Child Health, The University of Sheffield, Sheffield S10 2TH (United Kingdom); Dimitri, Paul [The Mellanby Centre for Bone Research, The University of Sheffield, Sheffield S10 2RX (United Kingdom); Frangi, Alejandro F. [Center for Computational Imaging and Simulation Technologies in Biomedicine, The University of Sheffield, Sheffield S1 3JD (United Kingdom)

    2016-06-15

    Purpose: This paper presents a statistical approach for the prediction of trabecular bone parameters from low-resolution multisequence magnetic resonance imaging (MRI) in children, thus addressing the limitations of high-resolution modalities such as HR-pQCT, including the significant exposure of young patients to radiation and the limited applicability of such modalities to peripheral bones in vivo. Methods: A statistical predictive model is constructed from a database of MRI and HR-pQCT datasets, to relate the low-resolution MRI appearance in the cancellous bone to the trabecular parameters extracted from the high-resolution images. The description of the MRI appearance is achieved between subjects by using a collection of feature descriptors, which describe the texture properties inside the cancellous bone, and which are invariant to the geometry and size of the trabecular areas. The predictive model is built by fitting to the training data a nonlinear partial least square regression between the input MRI features and the output trabecular parameters. Results: Detailed validation based on a sample of 96 datasets shows correlations >0.7 between the trabecular parameters predicted from low-resolution multisequence MRI based on the proposed statistical model and the values extracted from high-resolution HRp-QCT. Conclusions: The obtained results indicate the promise of the proposed predictive technique for the estimation of trabecular parameters in children from multisequence MRI, thus reducing the need for high-resolution radiation-based scans for a fragile population that is under development and growth.

  14. Estimation of error components in a multi-error linear regression model, with an application to track fitting

    International Nuclear Information System (INIS)

    Fruehwirth, R.

    1993-01-01

    We present an estimation procedure of the error components in a linear regression model with multiple independent stochastic error contributions. After solving the general problem we apply the results to the estimation of the actual trajectory in track fitting with multiple scattering. (orig.)

  15. Penalized estimation for competing risks regression with applications to high-dimensional covariates

    DEFF Research Database (Denmark)

    Ambrogi, Federico; Scheike, Thomas H.

    2016-01-01

    of competing events. The direct binomial regression model of Scheike and others (2008. Predicting cumulative incidence probability by direct binomial regression. Biometrika 95: (1), 205-220) is reformulated in a penalized framework to possibly fit a sparse regression model. The developed approach is easily...... Research 19: (1), 29-51), the research regarding competing risks is less developed (Binder and others, 2009. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25: (7), 890-896). The aim of this work is to consider how to do penalized regression in the presence...... implementable using existing high-performance software to do penalized regression. Results from simulation studies are presented together with an application to genomic data when the endpoint is progression-free survival. An R function is provided to perform regularized competing risks regression according...

  16. A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters.

    Science.gov (United States)

    Adin, A; Lee, D; Goicoa, T; Ugarte, María Dolores

    2018-01-01

    Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.

  17. Using a Regression Method for Estimating Performance in a Rapid Serial Visual Presentation Target-Detection Task

    Science.gov (United States)

    2017-12-01

    Fig. 2 Simulation method; the process for one iteration of the simulation . It was repeated 250 times per combination of HR and FAR. Analysis was...distribution is unlimited. 8 Fig. 2 Simulation method; the process for one iteration of the simulation . It was repeated 250 times per combination of HR...stimuli. Simulations show that this regression method results in an unbiased and accurate estimate of target detection performance. The regression

  18. A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

    Directory of Open Access Journals (Sweden)

    Jibo Yue

    2018-01-01

    Full Text Available Above-ground biomass (AGB provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index and eight statistical regression techniques: artificial neural network (ANN, multivariable linear regression (MLR, decision-tree regression (DT, boosted binary regression tree (BBRT, partial least squares regression (PLSR, random forest regression (RF, support vector machine regression (SVM, and principal component regression (PCR, which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise. Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE = 0.18; thus, PLSR is best suited for works requiring high

  19. Estimation of Total Nitrogen and Phosphorus in New England Streams Using Spatially Referenced Regression Models

    Science.gov (United States)

    Moore, Richard Bridge; Johnston, Craig M.; Robinson, Keith W.; Deacon, Jeffrey R.

    2004-01-01

    The U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA) and the New England Interstate Water Pollution Control Commission (NEIWPCC), has developed a water-quality model, called SPARROW (Spatially Referenced Regressions on Watershed Attributes), to assist in regional total maximum daily load (TMDL) and nutrient-criteria activities in New England. SPARROW is a spatially detailed, statistical model that uses regression equations to relate total nitrogen and phosphorus (nutrient) stream loads to nutrient sources and watershed characteristics. The statistical relations in these equations are then used to predict nutrient loads in unmonitored streams. The New England SPARROW models are built using a hydrologic network of 42,000 stream reaches and associated watersheds. Watershed boundaries are defined for each stream reach in the network through the use of a digital elevation model and existing digitized watershed divides. Nutrient source data is from permitted wastewater discharge data from USEPA's Permit Compliance System (PCS), various land-use sources, and atmospheric deposition. Physical watershed characteristics include drainage area, land use, streamflow, time-of-travel, stream density, percent wetlands, slope of the land surface, and soil permeability. The New England SPARROW models for total nitrogen and total phosphorus have R-squared values of 0.95 and 0.94, with mean square errors of 0.16 and 0.23, respectively. Variables that were statistically significant in the total nitrogen model include permitted municipal-wastewater discharges, atmospheric deposition, agricultural area, and developed land area. Total nitrogen stream-loss rates were significant only in streams with average annual flows less than or equal to 2.83 cubic meters per second. In streams larger than this, there is nondetectable in-stream loss of annual total nitrogen in New England. Variables that were statistically significant in the total

  20. A note on modeling of tumor regression for estimation of radiobiological parameters

    International Nuclear Information System (INIS)

    Zhong, Hualiang; Chetty, Indrin

    2014-01-01

    Purpose: Accurate calculation of radiobiological parameters is crucial to predicting radiation treatment response. Modeling differences may have a significant impact on derived parameters. In this study, the authors have integrated two existing models with kinetic differential equations to formulate a new tumor regression model for estimation of radiobiological parameters for individual patients. Methods: A system of differential equations that characterizes the birth-and-death process of tumor cells in radiation treatment was analytically solved. The solution of this system was used to construct an iterative model (Z-model). The model consists of three parameters: tumor doubling time T d , half-life of dead cells T r , and cell survival fraction SF D under dose D. The Jacobian determinant of this model was proposed as a constraint to optimize the three parameters for six head and neck cancer patients. The derived parameters were compared with those generated from the two existing models: Chvetsov's model (C-model) and Lim's model (L-model). The C-model and L-model were optimized with the parameter T d fixed. Results: With the Jacobian-constrained Z-model, the mean of the optimized cell survival fractions is 0.43 ± 0.08, and the half-life of dead cells averaged over the six patients is 17.5 ± 3.2 days. The parameters T r and SF D optimized with the Z-model differ by 1.2% and 20.3% from those optimized with the T d -fixed C-model, and by 32.1% and 112.3% from those optimized with the T d -fixed L-model, respectively. Conclusions: The Z-model was analytically constructed from the differential equations of cell populations that describe changes in the number of different tumor cells during the course of radiation treatment. The Jacobian constraints were proposed to optimize the three radiobiological parameters. The generated model and its optimization method may help develop high-quality treatment regimens for individual patients

  1. An Integrated Approach to Battery Health Monitoring using Bayesian Regression, Classification and State Estimation

    Data.gov (United States)

    National Aeronautics and Space Administration — The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to...

  2. Testing and Estimating Shape-Constrained Nonparametric Density and Regression in the Presence of Measurement Error

    KAUST Repository

    Carroll, Raymond J.; Delaigle, Aurore; Hall, Peter

    2011-01-01

    In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment

  3. Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

    Science.gov (United States)

    Lusiana, Evellin Dewi

    2017-12-01

    The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.

  4. Skeletal height estimation from regression analysis of sternal lengths in a Northwest Indian population of Chandigarh region: a postmortem study.

    Science.gov (United States)

    Singh, Jagmahender; Pathak, R K; Chavali, Krishnadutt H

    2011-03-20

    Skeletal height estimation from regression analysis of eight sternal lengths in the subjects of Chandigarh zone of Northwest India is the topic of discussion in this study. Analysis of eight sternal lengths (length of manubrium, length of mesosternum, combined length of manubrium and mesosternum, total sternal length and first four intercostals lengths of mesosternum) measured from 252 male and 91 female sternums obtained at postmortems revealed that mean cadaver stature and sternal lengths were more in North Indians and males than the South Indians and females. Except intercostal lengths, all the sternal lengths were positively correlated with stature of the deceased in both sexes (P regression analysis of sternal lengths was found more useful than the linear regression for stature estimation. Using multivariate regression analysis, the combined length of manubrium and mesosternum in both sexes and the length of manubrium along with 2nd and 3rd intercostal lengths of mesosternum in males were selected as best estimators of stature. Nonetheless, the stature of males can be predicted with SEE of 6.66 (R(2) = 0.16, r = 0.318) from combination of MBL+BL_3+LM+BL_2, and in females from MBL only, it can be estimated with SEE of 6.65 (R(2) = 0.10, r = 0.318), whereas from the multiple regression analysis of pooled data, stature can be known with SEE of 6.97 (R(2) = 0.387, r = 575) from the combination of MBL+LM+BL_2+TSL+BL_3. The R(2) and F-ratio were found to be statistically significant for almost all the variables in both the sexes, except 4th intercostal length in males and 2nd to 4th intercostal lengths in females. The 'major' sternal lengths were more useful than the 'minor' ones for stature estimation The universal regression analysis used by Kanchan et al. [39] when applied to sternal lengths, gave satisfactory estimates of stature for males only but female stature was comparatively better estimated from simple linear regressions. But they are not proposed for the

  5. Simple estimation procedures for regression analysis of interval-censored failure time data under the proportional hazards model.

    Science.gov (United States)

    Sun, Jianguo; Feng, Yanqin; Zhao, Hui

    2015-01-01

    Interval-censored failure time data occur in many fields including epidemiological and medical studies as well as financial and sociological studies, and many authors have investigated their analysis (Sun, The statistical analysis of interval-censored failure time data, 2006; Zhang, Stat Modeling 9:321-343, 2009). In particular, a number of procedures have been developed for regression analysis of interval-censored data arising from the proportional hazards model (Finkelstein, Biometrics 42:845-854, 1986; Huang, Ann Stat 24:540-568, 1996; Pan, Biometrics 56:199-203, 2000). For most of these procedures, however, one drawback is that they involve estimation of both regression parameters and baseline cumulative hazard function. In this paper, we propose two simple estimation approaches that do not need estimation of the baseline cumulative hazard function. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is conducted and indicates that they work well for practical situations.

  6. INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles

    KAUST Repository

    Opitz, Thomas; Huser, Raphaë l; Bakka, Haakon; Rue, Haavard

    2018-01-01

    approach is based on a Bayesian generalized additive modeling framework that is designed to estimate complex trends in marginal extremes over space and time. First, we estimate a high non-stationary threshold using a gamma distribution for precipitation

  7. Reducing Monte Carlo error in the Bayesian estimation of risk ratios using log-binomial regression models.

    Science.gov (United States)

    Salmerón, Diego; Cano, Juan A; Chirlaque, María D

    2015-08-30

    In cohort studies, binary outcomes are very often analyzed by logistic regression. However, it is well known that when the goal is to estimate a risk ratio, the logistic regression is inappropriate if the outcome is common. In these cases, a log-binomial regression model is preferable. On the other hand, the estimation of the regression coefficients of the log-binomial model is difficult owing to the constraints that must be imposed on these coefficients. Bayesian methods allow a straightforward approach for log-binomial regression models and produce smaller mean squared errors in the estimation of risk ratios than the frequentist methods, and the posterior inferences can be obtained using the software WinBUGS. However, Markov chain Monte Carlo methods implemented in WinBUGS can lead to large Monte Carlo errors in the approximations to the posterior inferences because they produce correlated simulations, and the accuracy of the approximations are inversely related to this correlation. To reduce correlation and to improve accuracy, we propose a reparameterization based on a Poisson model and a sampling algorithm coded in R. Copyright © 2015 John Wiley & Sons, Ltd.

  8. Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis.

    Science.gov (United States)

    Khalil, Mohamed H; Shebl, Mostafa K; Kosba, Mohamed A; El-Sabrout, Karim; Zaki, Nesma

    2016-08-01

    This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens' eggs. Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens.

  9. Genetic Parameters for Body condition score, Body weigth, Milk yield and Fertility estimated using random regression models

    NARCIS (Netherlands)

    Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.

    2003-01-01

    Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields

  10. On the use of a regression model for trend estimates from ground-based atmospheric observations in the Southern hemisphere

    CSIR Research Space (South Africa)

    Bencherif, H

    2010-09-01

    Full Text Available The present reports on the use of a multi-regression model adapted at Reunion University for temperature and ozone trend estimates. Depending on the location of the observing site, the studied geophysical signal is broken down in form of a sum...

  11. Comparison of Regression Analysis and Transfer Function in Estimating the Parameters of Central Pulse Waves from Brachial Pulse Wave.

    Science.gov (United States)

    Chai, Rui; Xu, Li-Sheng; Yao, Yang; Hao, Li-Ling; Qi, Lin

    2017-01-01

    This study analyzed ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO), and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. Invasively measured parameters were compared with parameters measured from brachial pulse waves by regression model and transfer function model. Accuracy of parameters estimated by regression and transfer function model, was compared too. Findings showed that k value, central pulse wave and brachial pulse wave parameters invasively measured, correlated positively. Regression model parameters including A_slope, DBP, SEVR, and transfer function model parameters had good consistency with parameters invasively measured. They had same effect of consistency. SBP, PP, SV, and CO could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

  12. Combination of supervised and semi-supervised regression models for improved unbiased estimation

    DEFF Research Database (Denmark)

    Arenas-Garía, Jeronimo; Moriana-Varo, Carlos; Larsen, Jan

    2010-01-01

    In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised and semisupervi......In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised...

  13. Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

    Science.gov (United States)

    Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett

    2009-01-01

    Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....

  14. Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

    Science.gov (United States)

    Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

    2013-01-01

    Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

  15. Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression

    Science.gov (United States)

    Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M.

    2013-01-01

    Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…

  16. Maximum likelihood estimation for Cox's regression model under nested case-control sampling

    DEFF Research Database (Denmark)

    Scheike, Thomas; Juul, Anders

    2004-01-01

    Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional hazard...

  17. Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.

    Science.gov (United States)

    Luque-Fernandez, Miguel Angel; Belot, Aurélien; Quaresma, Manuela; Maringe, Camille; Coleman, Michel P; Rachet, Bernard

    2016-10-01

    In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion.

  18. Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks

    International Nuclear Information System (INIS)

    Elliott Campbell, J.; Moen, Jeremie C.; Ney, Richard A.; Schnoor, Jerald L.

    2008-01-01

    Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively. - Large differences in estimates of soil organic carbon stocks and annual changes in stocks for Wisconsin forestlands indicate a need for validation from forthcoming forest surveys

  19. A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

    Science.gov (United States)

    Karabatsos, George

    2017-02-01

    Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

  20. Sub-pixel estimation of tree cover and bare surface densities using regression tree analysis

    Directory of Open Access Journals (Sweden)

    Carlos Augusto Zangrando Toneli

    2011-09-01

    Full Text Available Sub-pixel analysis is capable of generating continuous fields, which represent the spatial variability of certain thematic classes. The aim of this work was to develop numerical models to represent the variability of tree cover and bare surfaces within the study area. This research was conducted in the riparian buffer within a watershed of the São Francisco River in the North of Minas Gerais, Brazil. IKONOS and Landsat TM imagery were used with the GUIDE algorithm to construct the models. The results were two index images derived with regression trees for the entire study area, one representing tree cover and the other representing bare surface. The use of non-parametric and non-linear regression tree models presented satisfactory results to characterize wetland, deciduous and savanna patterns of forest formation.

  1. Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales

    Czech Academy of Sciences Publication Activity Database

    Krištoufek, Ladislav

    2015-01-01

    Roč. 91, č. 1 (2015), 022802-1-022802-5 ISSN 1539-3755 R&D Projects: GA ČR(CZ) GP14-11402P Grant - others:GA ČR(CZ) GAP402/11/0948 Program:GA Institutional support: RVO:67985556 Keywords : Detrended cross-correlation analysis * Regression * Scales Subject RIV: AH - Economics Impact factor: 2.288, year: 2014 http://library.utia.cas.cz/separaty/2015/E/kristoufek-0452315.pdf

  2. Estimating transmitted waves of floating breakwater using support vector regression model

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Hegde, A.V.; Kumar, V.; Patil, S.G.

    is first mapped onto an m-dimensional feature space using some fixed (nonlinear) mapping, and then a linear model is constructed in this feature space (Ivanciuc Ovidiu 2007). Using mathematical notation, the linear model in the feature space f(x, w... regressive vector machines, Ocean Engineering Journal, Vol – 36, pp 339 – 347, 2009. 3. Ivanciuc Ovidiu, Applications of support vector machines in chemistry, Review in Computational Chemistry, Eds K. B. Lipkouitz and T. R. Cundari, Vol – 23...

  3. Estimation of Stature from Footprint Anthropometry Using Regression Analysis: A Study on the Bidayuh Population of East Malaysia

    Directory of Open Access Journals (Sweden)

    T. Nataraja Moorthy

    2015-05-01

    Full Text Available The human foot has been studied for a variety of reasons, i.e., for forensic as well as non-forensic purposes by anatomists, forensic scientists, anthropologists, physicians, podiatrists, and numerous other groups. An aspect of human identification that has received scant attention from forensic anthropologists is the study of human feet and the footprints made by the feet. The present study, conducted during 2013-2014, aimed to derive population specific regression equations to estimate stature from the footprint anthropometry of indigenous adult Bidayuhs in the east of Malaysia. The study sample consisted of 480 bilateral footprints collected using a footprint kit from 240 Bidayuhs (120 males and 120 females, who consented to taking part in the study. Their ages ranged from 18 to 70 years. Stature was measured using a portable body meter device (SECA model 206. The data were analyzed using PASW Statistics version 20. In this investigation, better results were obtained in terms of correlation coefficient (R between stature and various footprint measurements and regression analysis in estimating the stature. The (R values showed a positive and statistically significant (p < 0.001 relationship between the two parameters. The correlation coefficients in the pooled sample (0.861–0.882 were comparatively higher than those of an individual male (0.762-0.795 and female (0.722-0.765. This study provided regression equations to estimate stature from footprints in the Bidayuh population. The result showed that the regression equations without sex indicators performed significantly better than models with gender indications. The regression equations derived for a pooled sample can be used to estimate stature, even when the sex of the footprint is unknown, as in real crime scenes.

  4. Recursive least squares method of regression coefficients estimation as a special case of Kalman filter

    Science.gov (United States)

    Borodachev, S. M.

    2016-06-01

    The simple derivation of recursive least squares (RLS) method equations is given as special case of Kalman filter estimation of a constant system state under changing observation conditions. A numerical example illustrates application of RLS to multicollinearity problem.

  5. Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

    Science.gov (United States)

    Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

    2017-09-01

    To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

  6. A comparison of the performances of an artificial neural network and a regression model for GFR estimation.

    Science.gov (United States)

    Liu, Xun; Li, Ning-shan; Lv, Lin-sheng; Huang, Jian-hua; Tang, Hua; Chen, Jin-xia; Ma, Hui-juan; Wu, Xiao-ming; Lou, Tan-qi

    2013-12-01

    Accurate estimation of glomerular filtration rate (GFR) is important in clinical practice. Current models derived from regression are limited by the imprecision of GFR estimates. We hypothesized that an artificial neural network (ANN) might improve the precision of GFR estimates. A study of diagnostic test accuracy. 1,230 patients with chronic kidney disease were enrolled, including the development cohort (n=581), internal validation cohort (n=278), and external validation cohort (n=371). Estimated GFR (eGFR) using a new ANN model and a new regression model using age, sex, and standardized serum creatinine level derived in the development and internal validation cohort, and the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) 2009 creatinine equation. Measured GFR (mGFR). GFR was measured using a diethylenetriaminepentaacetic acid renal dynamic imaging method. Serum creatinine was measured with an enzymatic method traceable to isotope-dilution mass spectrometry. In the external validation cohort, mean mGFR was 49±27 (SD) mL/min/1.73 m2 and biases (median difference between mGFR and eGFR) for the CKD-EPI, new regression, and new ANN models were 0.4, 1.5, and -0.5 mL/min/1.73 m2, respectively (P30% from mGFR) were 50.9%, 77.4%, and 78.7%, respectively (Psource of systematic bias in comparisons of new models to CKD-EPI, and both the derivation and validation cohorts consisted of a group of patients who were referred to the same institution. An ANN model using 3 variables did not perform better than a new regression model. Whether ANN can improve GFR estimation using more variables requires further investigation. Copyright © 2013 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

  7. Estimating the causes of traffic accidents using logistic regression and discriminant analysis.

    Science.gov (United States)

    Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu

    2014-01-01

    Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.

  8. A constrained polynomial regression procedure for estimating the local False Discovery Rate

    Directory of Open Access Journals (Sweden)

    Broët Philippe

    2007-06-01

    Full Text Available Abstract Background In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR, which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The lFDR not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochberg's False Discovery Rate (FDR for subsets of genes. Results In the framework of estimating procedures without any distributional assumption under the alternative hypothesis, a new and efficient procedure for estimating the lFDR is described. The results of a simulation study indicated good performances for the proposed estimator in comparison to four published ones. The five different procedures were applied to real datasets. Conclusion A novel and efficient procedure for estimating lFDR was developed and evaluated.

  9. Maximum likelihood estimation for Cox's regression model under nested case-control sampling

    DEFF Research Database (Denmark)

    Scheike, Thomas Harder; Juul, Anders

    2004-01-01

    -like growth factor I was associated with ischemic heart disease. The study was based on a population of 3784 Danes and 231 cases of ischemic heart disease where controls were matched on age and gender. We illustrate the use of the MLE for these data and show how the maximum likelihood framework can be used......Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional hazards...... model. The MLE is computed by the EM-algorithm, which is easy to implement in the proportional hazards setting. Standard errors are estimated by a numerical profile likelihood approach based on EM aided differentiation. The work was motivated by a nested case-control study that hypothesized that insulin...

  10. Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

    Science.gov (United States)

    Visser, H.; Molenaar, J.

    1995-05-01

    The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of

  11. Allelic drop-out probabilities estimated by logistic regression--Further considerations and practical implementation

    DEFF Research Database (Denmark)

    Tvedebrink, Torben; Eriksen, Poul Svante; Asplund, Maria

    2012-01-01

    We discuss the model for estimating drop-out probabilities presented by Tvedebrink et al. [7] and the concerns, that have been raised. The criticism of the model has demonstrated that the model is not perfect. However, the model is very useful for advanced forensic genetic work, where allelic drop-out...... is occurring. With this discussion, we hope to improve the drop-out model, so that it can be used for practical forensic genetics and stimulate further discussions. We discuss how to estimate drop-out probabilities when using a varying number of PCR cycles and other experimental conditions....

  12. Normalization Ridge Regression in Practice II: The Estimation of Multiple Feedback Linkages.

    Science.gov (United States)

    Bulcock, J. W.

    The use of the two-stage least squares (2 SLS) procedure for estimating nonrecursive social science models is often impractical when multiple feedback linkages are required. This is because 2 SLS is extremely sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is a biased, variance reduced procedure…

  13. Linear regressive model structures for estimation and prediction of compartmental diffusive systems

    NARCIS (Netherlands)

    Vries, D; Keesman, K.J.; Zwart, Heiko J.

    In input-output relations of (compartmental) diffusive systems, physical parameters appear non-linearly, resulting in the use of (constrained) non-linear parameter estimation techniques with its short-comings regarding global optimality and computational effort. Given a LTI system in state space

  14. Linear regressive model structures for estimation and prediction of compartmental diffusive systems

    NARCIS (Netherlands)

    Vries, D.; Keesman, K.J.; Zwart, H.

    2006-01-01

    Abstract In input-output relations of (compartmental) diffusive systems, physical parameters appear non-linearly, resulting in the use of (constrained) non-linear parameter estimation techniques with its short-comings regarding global optimality and computational effort. Given a LTI system in state

  15. Estimation of residual stress in welding of dissimilar metals at nuclear power plants using cascaded support vetor regression

    Energy Technology Data Exchange (ETDEWEB)

    Koo, Young Do; Yoo, Kwae Hwan; Na, Man Gyun [Dept. of Nuclear Engineering, Chosun University, Gwangju (Korea, Republic of)

    2017-06-15

    Residual stress is a critical element in determining the integrity of parts and the lifetime of welded structures. It is necessary to estimate the residual stress of a welding zone because residual stress is a major reason for the generation of primary water stress corrosion cracking in nuclear power plants. That is, it is necessary to estimate the distribution of the residual stress in welding of dissimilar metals under manifold welding conditions. In this study, a cascaded support vector regression (CSVR) model was presented to estimate the residual stress of a welding zone. The CSVR model was serially and consecutively structured in terms of SVR modules. Using numerical data obtained from finite element analysis by a subtractive clustering method, learning data that explained the characteristic behavior of the residual stress of a welding zone were selected to optimize the proposed model. The results suggest that the CSVR model yielded a better estimation performance when compared with a classic SVR model.

  16. Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

    Science.gov (United States)

    Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

    2017-01-01

    Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of

  17. Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

    Science.gov (United States)

    Lo, Ching F.

    1999-01-01

    The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

  18. Fragility estimation for seismically isolated nuclear structures by high confidence low probability of failure values and bi-linear regression

    International Nuclear Information System (INIS)

    Carausu, A.

    1996-01-01

    A method for the fragility estimation of seismically isolated nuclear power plant structure is proposed. The relationship between the ground motion intensity parameter (e.g. peak ground velocity or peak ground acceleration) and the response of isolated structures is expressed in terms of a bi-linear regression line, whose coefficients are estimated by the least-square method in terms of available data on seismic input and structural response. The notion of high confidence low probability of failure (HCLPF) value is also used for deriving compound fragility curves for coupled subsystems. (orig.)

  19. Power system state estimation using an iteratively reweighted least squares method for sequential L{sub 1}-regression

    Energy Technology Data Exchange (ETDEWEB)

    Jabr, R.A. [Electrical, Computer and Communication Engineering Department, Notre Dame University, P.O. Box 72, Zouk Mikhael, Zouk Mosbeh (Lebanon)

    2006-02-15

    This paper presents an implementation of the least absolute value (LAV) power system state estimator based on obtaining a sequence of solutions to the L{sub 1}-regression problem using an iteratively reweighted least squares (IRLS{sub L1}) method. The proposed implementation avoids reformulating the regression problem into standard linear programming (LP) form and consequently does not require the use of common methods of LP, such as those based on the simplex method or interior-point methods. It is shown that the IRLS{sub L1} method is equivalent to solving a sequence of linear weighted least squares (LS) problems. Thus, its implementation presents little additional effort since the sparse LS solver is common to existing LS state estimators. Studies on the termination criteria of the IRLS{sub L1} method have been carried out to determine a procedure for which the proposed estimator is more computationally efficient than a previously proposed non-linear iteratively reweighted least squares (IRLS) estimator. Indeed, it is revealed that the proposed method is a generalization of the previously reported IRLS estimator, but is based on more rigorous theory. (author)

  20. Beyond the mean estimate: a quantile regression analysis of inequalities in educational outcomes using INVALSI survey data

    Directory of Open Access Journals (Sweden)

    Antonella Costanzo

    2017-09-01

    Full Text Available Abstract The number of studies addressing issues of inequality in educational outcomes using cognitive achievement tests and variables from large-scale assessment data has increased. Here the value of using a quantile regression approach is compared with a classical regression analysis approach to study the relationships between educational outcomes and likely predictor variables. Italian primary school data from INVALSI large-scale assessments were analyzed using both quantile and standard regression approaches. Mathematics and reading scores were regressed on students' characteristics and geographical variables selected for their theoretical and policy relevance. The results demonstrated that, in Italy, the role of gender and immigrant status varied across the entire conditional distribution of students’ performance. Analogous results emerged pertaining to the difference in students’ performance across Italian geographic areas. These findings suggest that quantile regression analysis is a useful tool to explore the determinants and mechanisms of inequality in educational outcomes. A proper interpretation of quantile estimates may enable teachers to identify effective learning activities and help policymakers to develop tailored programs that increase equity in education.

  1. An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran

    Energy Technology Data Exchange (ETDEWEB)

    Azadeh, A; Seraj, O [Department of Industrial Engineering and Research Institute of Energy Management and Planning, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, University of Tehran, P.O. Box 11365-4563 (Iran); Saberi, M [Department of Industrial Engineering, University of Tafresh (Iran); Institute for Digital Ecosystems and Business Intelligence, Curtin University of Technology, Perth (Australia)

    2010-06-15

    This study presents an integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression (FR) or time series and the integrated algorithm could be an ideal substitute for such cases. At First, preferred Time series model is selected from linear or nonlinear models. For this, after selecting preferred Auto Regression Moving Average (ARMA) model, Mcleod-Li test is applied to determine nonlinearity condition. When, nonlinearity condition is satisfied, the preferred nonlinear model is selected and defined as preferred time series model. At last, the preferred model from fuzzy regression and time series model is selected by the Granger-Newbold. Also, the impact of data preprocessing on the fuzzy regression performance is considered. Monthly electricity consumption of Iran from March 1994 to January 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with other intelligent tools such as Genetic Algorithm (GA) and Artificial Neural Network (ANN). (author)

  2. Regression analysis and transfer function in estimating the parameters of central pulse waves from brachial pulse wave.

    Science.gov (United States)

    Chai Rui; Li Si-Man; Xu Li-Sheng; Yao Yang; Hao Li-Ling

    2017-07-01

    This study mainly analyzed the parameters such as ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO) and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. These parameters extracted from the central pulse wave invasively measured were compared with the parameters measured from the brachial pulse waves by a regression model and a transfer function model. The accuracy of the parameters which were estimated by the regression model and the transfer function model was compared too. Our findings showed that in addition to the k value, the above parameters of the central pulse wave and the brachial pulse wave invasively measured had positive correlation. Both the regression model parameters including A_slope, DBP, SEVR and the transfer function model parameters had good consistency with the parameters invasively measured, and they had the same effect of consistency. The regression equations of the three parameters were expressed by Y'=a+bx. The SBP, PP, SV, CO of central pulse wave could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

  3. Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data

    Science.gov (United States)

    Sanford, Ward E.; Selnick, David L.

    2013-01-01

    Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water-balance method was combined with a climate and land-cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971-2000 across the U.S. to obtain long-term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land-cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land-cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land-cover data can also improve those predictions. Using the climate and land-cover data at an 800-m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land-cover data are plentiful.

  4. Applied Prevalence Ratio estimation with different Regression models: An example from a cross-national study on substance use research.

    Science.gov (United States)

    Espelt, Albert; Marí-Dell'Olmo, Marc; Penelo, Eva; Bosque-Prous, Marina

    2016-06-14

    To examine the differences between Prevalence Ratio (PR) and Odds Ratio (OR) in a cross-sectional study and to provide tools to calculate PR using two statistical packages widely used in substance use research (STATA and R). We used cross-sectional data from 41,263 participants of 16 European countries participating in the Survey on Health, Ageing and Retirement in Europe (SHARE). The dependent variable, hazardous drinking, was calculated using the Alcohol Use Disorders Identification Test - Consumption (AUDIT-C). The main independent variable was gender. Other variables used were: age, educational level and country of residence. PR of hazardous drinking in men with relation to women was estimated using Mantel-Haenszel method, log-binomial regression models and poisson regression models with robust variance. These estimations were compared to the OR calculated using logistic regression models. Prevalence of hazardous drinkers varied among countries. Generally, men have higher prevalence of hazardous drinking than women [PR=1.43 (1.38-1.47)]. Estimated PR was identical independently of the method and the statistical package used. However, OR overestimated PR, depending on the prevalence of hazardous drinking in the country. In cross-sectional studies, where comparisons between countries with differences in the prevalence of the disease or condition are made, it is advisable to use PR instead of OR.

  5. Biomass estimates of freshwater zooplankton from length-carbon regression equations

    Directory of Open Access Journals (Sweden)

    Patrizia COMOLI

    2000-02-01

    Full Text Available We present length/carbon regression equations of zooplankton species collected from Lake Maggiore (N. Italy during 1992. The results are discussed in terms of the environmental factors, e.g. food availability, predation, controlling biomass production of particle- feeders and predators in the pelagic system of lakes. The marked seasonality in the length-standardized carbon content of Daphnia, and its time-specific trend suggest that from spring onward food availability for Daphnia population may be regarded as a simple decay function. Seasonality does not affect the carbon content/unit length of the two predator Cladocera Leptodora kindtii and Bythotrephes longimanus. Predation is probably the most important regulating factor for the seasonal dynamics of their carbon biomass. The existence of a constant factor to convert the diameter of Conochilus colonies into carbon seems reasonable for an organism whose population comes on quickly and just as quickly disappears.

  6. A case study to estimate costs using Neural Networks and regression based models

    Directory of Open Access Journals (Sweden)

    Nadia Bhuiyan

    2012-07-01

    Full Text Available Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.

  7. Dynamical System and Nonlinear Regression for Estimate Host-Parasitoid Relationship

    Directory of Open Access Journals (Sweden)

    Ileana Miranda Cabrera

    2010-01-01

    Full Text Available The complex relationships of a crop with the pest, its natural enemies, and the climate factors exist in all the ecosystems, but the mathematic models has studied only some components to know the relation cause-effect. The most studied system has been concerned with the relationship pest-natural enemies such as prey-predator or host-parasitoid. The present paper shows a dynamical system for studying the relationship host-parasitoid (Diaphorina citri, Tamarixia radiata and shows that a nonlinear model permits the estimation of the parasite nymphs using nymphs healthy as the known variable. The model showed the functional answer of the parasitoid, in which a point arrives that its density is not augmented although the number host increases, and it becomes necessary to intervene in the ecosystem. A simple algorithm is used to estimate the parasitoids level using the priori relationship between the host and the climate factors and then the nonlinear model.

  8. Multivariate regression methods for estimating velocity of ictal discharges from human microelectrode recordings

    Science.gov (United States)

    Liou, Jyun-you; Smith, Elliot H.; Bateman, Lisa M.; McKhann, Guy M., II; Goodman, Robert R.; Greger, Bradley; Davis, Tyler S.; Kellis, Spencer S.; House, Paul A.; Schevon, Catherine A.

    2017-08-01

    Objective. Epileptiform discharges, an electrophysiological hallmark of seizures, can propagate across cortical tissue in a manner similar to traveling waves. Recent work has focused attention on the origination and propagation patterns of these discharges, yielding important clues to their source location and mechanism of travel. However, systematic studies of methods for measuring propagation are lacking. Approach. We analyzed epileptiform discharges in microelectrode array recordings of human seizures. The array records multiunit activity and local field potentials at 400 micron spatial resolution, from a small cortical site free of obstructions. We evaluated several computationally efficient statistical methods for calculating traveling wave velocity, benchmarking them to analyses of associated neuronal burst firing. Main results. Over 90% of discharges met statistical criteria for propagation across the sampled cortical territory. Detection rate, direction and speed estimates derived from a multiunit estimator were compared to four field potential-based estimators: negative peak, maximum descent, high gamma power, and cross-correlation. Interestingly, the methods that were computationally simplest and most efficient (negative peak and maximal descent) offer non-inferior results in predicting neuronal traveling wave velocities compared to the other two, more complex methods. Moreover, the negative peak and maximal descent methods proved to be more robust against reduced spatial sampling challenges. Using least absolute deviation in place of least squares error minimized the impact of outliers, and reduced the discrepancies between local field potential-based and multiunit estimators. Significance. Our findings suggest that ictal epileptiform discharges typically take the form of exceptionally strong, rapidly traveling waves, with propagation detectable across millimeter distances. The sequential activation of neurons in space can be inferred from clinically

  9. Estimating the prevalence of 26 health-related indicators at neighbourhood level in the Netherlands using structured additive regression.

    Science.gov (United States)

    van de Kassteele, Jan; Zwakhals, Laurens; Breugelmans, Oscar; Ameling, Caroline; van den Brink, Carolien

    2017-07-01

    Local policy makers increasingly need information on health-related indicators at smaller geographic levels like districts or neighbourhoods. Although more large data sources have become available, direct estimates of the prevalence of a health-related indicator cannot be produced for neighbourhoods for which only small samples or no samples are available. Small area estimation provides a solution, but unit-level models for binary-valued outcomes that can handle both non-linear effects of the predictors and spatially correlated random effects in a unified framework are rarely encountered. We used data on 26 binary-valued health-related indicators collected on 387,195 persons in the Netherlands. We associated the health-related indicators at the individual level with a set of 12 predictors obtained from national registry data. We formulated a structured additive regression model for small area estimation. The model captured potential non-linear relations between the predictors and the outcome through additive terms in a functional form using penalized splines and included a term that accounted for spatially correlated heterogeneity between neighbourhoods. The registry data were used to predict individual outcomes which in turn are aggregated into higher geographical levels, i.e. neighbourhoods. We validated our method by comparing the estimated prevalences with observed prevalences at the individual level and by comparing the estimated prevalences with direct estimates obtained by weighting methods at municipality level. We estimated the prevalence of the 26 health-related indicators for 415 municipalities, 2599 districts and 11,432 neighbourhoods in the Netherlands. We illustrate our method on overweight data and show that there are distinct geographic patterns in the overweight prevalence. Calibration plots show that the estimated prevalences agree very well with observed prevalences at the individual level. The estimated prevalences agree reasonably well with the

  10. INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles

    KAUST Repository

    Opitz, Thomas

    2018-05-25

    This work is motivated by the challenge organized for the 10th International Conference on Extreme-Value Analysis (EVA2017) to predict daily precipitation quantiles at the 99.8% level for each month at observed and unobserved locations. Our approach is based on a Bayesian generalized additive modeling framework that is designed to estimate complex trends in marginal extremes over space and time. First, we estimate a high non-stationary threshold using a gamma distribution for precipitation intensities that incorporates spatial and temporal random effects. Then, we use the Bernoulli and generalized Pareto (GP) distributions to model the rate and size of threshold exceedances, respectively, which we also assume to vary in space and time. The latent random effects are modeled additively using Gaussian process priors, which provide high flexibility and interpretability. We develop a penalized complexity (PC) prior specification for the tail index that shrinks the GP model towards the exponential distribution, thus preventing unrealistically heavy tails. Fast and accurate estimation of the posterior distributions is performed thanks to the integrated nested Laplace approximation (INLA). We illustrate this methodology by modeling the daily precipitation data provided by the EVA2017 challenge, which consist of observations from 40 stations in the Netherlands recorded during the period 1972–2016. Capitalizing on INLA’s fast computational capacity and powerful distributed computing resources, we conduct an extensive cross-validation study to select the model parameters that govern the smoothness of trends. Our results clearly outperform simple benchmarks and are comparable to the best-scoring approaches of the other teams.

  11. Empirical methods for the estimation of Southern Ocean CO2: support vector and random forest regression

    CSIR Research Space (South Africa)

    Gregor, Luke

    2017-12-01

    Full Text Available understanding with spatially integrated air–sea flux estimates (Fay and McKinley, 2014). Conversely, ocean biogeochemical process models are good tools for mechanis- tic understanding, but fail to represent the seasonality of CO2 fluxes in the Southern Ocean... of including coordinate variables as proxies of 1pCO2 in the empirical methods. In the inter- comparison study by Rödenbeck et al. (2015) proxies typi- cally include, but are not limited to, sea surface temperature (SST), chlorophyll a (Chl a), mixed layer...

  12. Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

    Directory of Open Access Journals (Sweden)

    Marjan Čeh

    2018-05-01

    Full Text Available The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008–2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1 the non-linear nature of the prediction assignment task; (2 input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3 the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R2 values, sales ratios, mean average percentage error (MAPE, coefficient of dispersion (COD revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.

  13. Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques.

    Science.gov (United States)

    Jones, Kelly W; Lewis, David J

    2015-01-01

    Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES)--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that

  14. Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques.

    Directory of Open Access Journals (Sweden)

    Kelly W Jones

    Full Text Available Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1 matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2 fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case

  15. A Technique for Estimating Intensity of Emotional Expressions and Speaking Styles in Speech Based on Multiple-Regression HSMM

    Science.gov (United States)

    Nose, Takashi; Kobayashi, Takao

    In this paper, we propose a technique for estimating the degree or intensity of emotional expressions and speaking styles appearing in speech. The key idea is based on a style control technique for speech synthesis using a multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse of the style control. In the proposed technique, the acoustic features of spectrum, power, fundamental frequency, and duration are simultaneously modeled using the MRHSMM. We derive an algorithm for estimating explanatory variables of the MRHSMM, each of which represents the degree or intensity of emotional expressions and speaking styles appearing in acoustic features of speech, based on a maximum likelihood criterion. We show experimental results to demonstrate the ability of the proposed technique using two types of speech data, simulated emotional speech and spontaneous speech with different speaking styles. It is found that the estimated values have correlation with human perception.

  16. The effect of PLS regression in PLS path model estimation when multicollinearity is present

    DEFF Research Database (Denmark)

    Nielsen, Rikke; Kristensen, Kai; Eskildsen, Jacob

    PLS path modelling has previously been found to be robust to multicollinearity both between latent variables and between manifest variables of a common latent variable (see e.g. Cassel et al. (1999), Kristensen, Eskildsen (2005), Westlund et al. (2008)). However, most of the studies investigate...... models with relatively few variables and very simple dependence structures compared to the models that are often estimated in practical settings. A recent study by Nielsen et al. (2009) found that when model structure is more complex, PLS path modelling is not as robust to multicollinearity between...... latent variables as previously assumed. A difference in the standard error of path coefficients of as much as 83% was found between moderate and severe levels of multicollinearity. Large differences were found not only for large path coefficients, but also for small path coefficients and in some cases...

  17. Using Multiple Regression in Estimating (semi) VOC Emissions and Concentrations at the European Scale

    DEFF Research Database (Denmark)

    Fauser, Patrik; Thomsen, Marianne; Pistocchi, Alberto

    2010-01-01

    chemicals available in the European Chemicals Bureau risk assessment reports (RARs). The method suggests a simple linear relationship between Henry's Law constant, octanol-water coefficient, use and production volumes, and emissions and PECs on a regional scale in the European Union. Emissions and PECs......This paper proposes a simple method for estimating emissions and predicted environmental concentrations (PECs) in water and air for organic chemicals that are used in household products and industrial processes. The method has been tested on existing data for 63 organic high-production volume...... are a result of a complex interaction between chemical properties, production and use patterns and geographical characteristics. A linear relationship cannot capture these complexities; however, it may be applied at a cost-efficient screening level for suggesting critical chemicals that are candidates...

  18. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

    Science.gov (United States)

    Yang, Duo; Zhang, Xu; Pan, Rui; Wang, Yujie; Chen, Zonghai

    2018-04-01

    The state-of-health (SOH) estimation is always a crucial issue for lithium-ion batteries. In order to provide an accurate and reliable SOH estimation, a novel Gaussian process regression (GPR) model based on charging curve is proposed in this paper. Different from other researches where SOH is commonly estimated by cycle life, in this work four specific parameters extracted from charging curves are used as inputs of the GPR model instead of cycle numbers. These parameters can reflect the battery aging phenomenon from different angles. The grey relational analysis method is applied to analyze the relational grade between selected features and SOH. On the other hand, some adjustments are made in the proposed GPR model. Covariance function design and the similarity measurement of input variables are modified so as to improve the SOH estimate accuracy and adapt to the case of multidimensional input. Several aging data from NASA data repository are used for demonstrating the estimation effect by the proposed method. Results show that the proposed method has high SOH estimation accuracy. Besides, a battery with dynamic discharging profile is used to verify the robustness and reliability of this method.

  19. Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques

    Science.gov (United States)

    Jones, Kelly W.; Lewis, David J.

    2015-01-01

    Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented—from protected areas to payments for ecosystem services (PES)—to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing ‘matching’ to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods—an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators—due to the presence of unobservable bias—that lead to differences in conclusions about effectiveness. The Ecuador case

  20. Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Tosun Erdi

    2017-01-01

    Full Text Available This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

  1. Estimation of nutrients and organic matter in Korean swine slurry using multiple regression analysis of physical and chemical properties.

    Science.gov (United States)

    Suresh, Arumuganainar; Choi, Hong Lim

    2011-10-01

    Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (Phydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste. Copyright © 2011 Elsevier Ltd. All rights reserved.

  2. Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals

    Science.gov (United States)

    Almirall, Daniel; Griffin, Beth Ann; McCaffrey, Daniel F.; Ramchand, Rajeev; Yuen, Robert A.; Murphy, Susan A.

    2014-01-01

    This article considers the problem of examining time-varying causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and possible confounders are time varying. The structural nested mean model (SNMM) is used to specify the moderated time-varying causal effects of interest in a conditional mean model for a continuous response given time-varying treatments and moderators. We present an easy-to-use estimator of the SNMM that combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time-varying causal effects if the time-varying moderators are also the sole time-varying confounders. The proposed IPTW+RR approach provides estimators of the moderated time-varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured time-varying confounders. We use a small simulation experiment to compare IPTW+RR versus the traditional regression approach and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time-varying moderators and time-varying confounders. We illustrate the methodology in a case study to assess if time-varying substance use moderates treatment effects on future substance use. PMID:23873437

  3. The estimation and prediction of the inventories for the liquid and gaseous radwaste systems using the linear regression analysis

    International Nuclear Information System (INIS)

    Kim, J. Y.; Shin, C. H.; Kim, J. K.; Lee, J. K.; Park, Y. J.

    2003-01-01

    The variation transitions of the inventories for the liquid radwaste system and the radioactive gas have being released in containment, and their predictive values according to the operation histories of Yonggwang(YGN) 3 and 4 were analyzed by linear regression analysis methodology. The results show that the variation transitions of the inventories for those systems are linearly increasing according to the operation histories but the inventories released to the environment are considerably lower than the recommended values based on the FSAR suggestions. It is considered that some conservation were presented in the estimation methodology in preparing stage of FSAR

  4. Use of Geographically Weighted Regression (GWR Method to Estimate the Effects of Location Attributes on the Residential Property Values

    Directory of Open Access Journals (Sweden)

    Mohd Faris Dziauddin

    2017-07-01

    Full Text Available This study estimates the effect of locational attributes on residential property values in Kuala Lumpur, Malaysia. Geographically weighted regression (GWR enables the use of the local parameter rather than the global parameter to be estimated, with the results presented in map form. The results of this study reveal that residential property values are mainly determined by the property’s physical (structural attributes, but proximity to locational attributes also contributes marginally. The use of GWR in this study is considered a better approach than other methods to examine the effect of locational attributes on residential property values. GWR has the capability to produce meaningful results in which different locational attributes have differential spatial effects across a geographical area on residential property values. This method has the ability to determine the factors on which premiums depend, and in turn it can assist the government in taxation matters.

  5. Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran).

    Science.gov (United States)

    Motlagh, Mohadeseh Ghanbari; Kafaky, Sasan Babaie; Mataji, Asadollah; Akhavan, Reza

    2018-05-21

    Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.

  6. Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

    Directory of Open Access Journals (Sweden)

    Maria Gabriela Campolina Diniz Peixoto

    2014-05-01

    Full Text Available The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524 of test-day milk yield (TDMY from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects, whereas the contemporary group, calving age (linear and quadratic effects and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

  7. Using Structured Additive Regression Models to Estimate Risk Factors of Malaria: Analysis of 2010 Malawi Malaria Indicator Survey Data

    Science.gov (United States)

    Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

    2014-01-01

    Background After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. Methods We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Results Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. Conclusions The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities. PMID:24991915

  8. Role of regression model selection and station distribution on the estimation of oceanic anthropogenic carbon change by eMLR

    Directory of Open Access Journals (Sweden)

    Y. Plancherel

    2013-07-01

    Full Text Available Quantifying oceanic anthropogenic carbon uptake by monitoring interior dissolved inorganic carbon (DIC concentrations is complicated by the influence of natural variability. The "eMLR method" aims to address this issue by using empirical regression fits of the data instead of the data themselves, inferring the change in anthropogenic carbon in time by difference between predictions generated by the regressions at each time. The advantages of the method are that it provides in principle a means to filter out natural variability, which theoretically becomes the regression residuals, and a way to deal with sparsely and unevenly distributed data. The degree to which these advantages are realized in practice is unclear, however. The ability of the eMLR method to recover the anthropogenic carbon signal is tested here using a global circulation and biogeochemistry model in which the true signal is known. Results show that regression model selection is particularly important when the observational network changes in time. When the observational network is fixed, the likelihood that co-located systematic misfits between the empirical model and the underlying, yet unknown, true model cancel is greater, improving eMLR results. Changing the observational network modifies how the spatio-temporal variance pattern is captured by the respective datasets, resulting in empirical models that are dynamically or regionally inconsistent, leading to systematic errors. In consequence, the use of regression formulae that change in time to represent systematically best-fit models at all times does not guarantee the best estimates of anthropogenic carbon change if the spatial distributions of the stations emphasize hydrographic features differently in time. Other factors, such as a balanced and representative station coverage, vertical continuity of the regression formulae consistent with the hydrographic context and resiliency of the spatial distribution of the residual

  9. A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

    Science.gov (United States)

    Zhang, L; Liu, X J

    2016-06-03

    With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

  10. Project of integrity assessment of flawed components with structural discontinuity (IAF). Data book for estimation stress intensity factor. Surface crack on ICM housing for penetration in reactor vessel

    International Nuclear Information System (INIS)

    2012-12-01

    The project of Integrity Assessment of Flawed Components with Structural Discontinuity (IAF) was entrusted to Japan Power Engineering and Inspection Corporation (JAPEIC) from Nuclear and Industrial Safety Agency (NISA) and started from FY 2001. And then, it was taken over to Japan Nuclear Energy Safety Organization (JNES) which was established in October 2003 and carried out until FY 2007. In the IAF project, weld joints between nickel based alloys and low alloy steels around penetrations in reactor vessel, safe-end of nozzles and shroud supports were selected from among components and pipe arrangements in nuclear power plants, where high residual stresses were generated due to welding and complex structure. Residual stresses around of the weld joints were estimated by finite element analysis method (FEM) with a general modeling method, then the reasonability and the conservativeness was evaluated. In addition, for postulated surface crack of stress corrosion cracking (SCC), a simple calculation method of stress intensity factor (K) required to estimate the crack growth was proposed and the effectiveness was confirmed. JNES compiled results of the IAF project into Data Books of Residual Stress Analysis of Weld Joint, and Data Book of Simplified Stress Intensity Factor Calculation for Penetration of Reactor as typical Structure Discontinuity, respectively. Data Books of Residual Stress Analysis in Weld Joint. 1. Butt Weld Joint of Small Diameter Cylinder (4B Sch40) (JNES-RE-2012-0005), 2. Dissimilar Metal Weld Joint in Safe End (One-Side Groove Joint (JNES-RE-2012-0006), 3. Dissimilar Metal Weld Joint in Safe End (Large Diameter Both-Side Groove Joint) (JNES-RE-2012-0007), 4. Weld Joint around Penetrations in Reactor Vessel (Insert Joint) (JNES-RE-2012-0008), 5. Weld Joint in Shroud Support (H8, H9, H10 and H11 Welds) (JNES-RE-2012-0009), 6. Analysis Model of Dissimilar Metal Weld Joint Applied Post Weld Heat Treatment (PWHT) (JNES-RE-2012-0010). Data Book of

  11. Study Heterogeneity and Estimation of Prevalence of Primary Aldosteronism: A Systematic Review and Meta-Regression Analysis.

    Science.gov (United States)

    Käyser, Sabine C; Dekkers, Tanja; Groenewoud, Hans J; van der Wilt, Gert Jan; Carel Bakx, J; van der Wel, Mark C; Hermus, Ad R; Lenders, Jacques W; Deinum, Jaap

    2016-07-01

    For health care planning and allocation of resources, realistic estimation of the prevalence of primary aldosteronism is necessary. Reported prevalences of primary aldosteronism are highly variable, possibly due to study heterogeneity. Our objective was to identify and explain heterogeneity in studies that aimed to establish the prevalence of primary aldosteronism in hypertensive patients. PubMed, EMBASE, Web of Science, Cochrane Library, and reference lists from January 1, 1990, to January 31, 2015, were used as data sources. Description of an adult hypertensive patient population with confirmed diagnosis of primary aldosteronism was included in this study. Dual extraction and quality assessment were the forms of data extraction. Thirty-nine studies provided data on 42 510 patients (nine studies, 5896 patients from primary care). Prevalence estimates varied from 3.2% to 12.7% in primary care and from 1% to 29.8% in referral centers. Heterogeneity was too high to establish point estimates (I(2) = 57.6% in primary care; 97.1% in referral centers). Meta-regression analysis showed higher prevalences in studies 1) published after 2000, 2) from Australia, 3) aimed at assessing prevalence of secondary hypertension, 4) that were retrospective, 5) that selected consecutive patients, and 6) not using a screening test. All studies had minor or major flaws. This study demonstrates that it is pointless to claim low or high prevalence of primary aldosteronism based on published reports. Because of the significant impact of a diagnosis of primary aldosteronism on health care resources and the necessary facilities, our findings urge for a prevalence study whose design takes into account the factors identified in the meta-regression analysis.

  12. Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

    Science.gov (United States)

    van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

    2017-03-21

    Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. SNOW DEPTH ESTIMATION USING TIME SERIES PASSIVE MICROWAVE IMAGERY VIA GENETICALLY SUPPORT VECTOR REGRESSION (CASE STUDY URMIA LAKE BASIN

    Directory of Open Access Journals (Sweden)

    N. Zahir

    2015-12-01

    Full Text Available Lake Urmia is one of the most important ecosystems of the country which is on the verge of elimination. Many factors contribute to this crisis among them is the precipitation, paly important roll. Precipitation has many forms one of them is in the form of snow. The snow on Sahand Mountain is one of the main and important sources of the Lake Urmia’s water. Snow Depth (SD is vital parameters for estimating water balance for future year. In this regards, this study is focused on SD parameter using Special Sensor Microwave/Imager (SSM/I instruments on board the Defence Meteorological Satellite Program (DMSP F16. The usual statistical methods for retrieving SD include linear and non-linear ones. These methods used least square procedure to estimate SD model. Recently, kernel base methods widely used for modelling statistical problem. From these methods, the support vector regression (SVR is achieved the high performance for modelling the statistical problem. Examination of the obtained data shows the existence of outlier in them. For omitting these outliers, wavelet denoising method is applied. After the omission of the outliers it is needed to select the optimum bands and parameters for SVR. To overcome these issues, feature selection methods have shown a direct effect on improving the regression performance. We used genetic algorithm (GA for selecting suitable features of the SSMI bands in order to estimate SD model. The results for the training and testing data in Sahand mountain is [R²_TEST=0.9049 and RMSE= 6.9654] that show the high SVR performance.

  14. Radiologic assessment of third molar tooth and spheno-occipital synchondrosis for age estimation: a multiple regression analysis study.

    Science.gov (United States)

    Demirturk Kocasarac, Husniye; Sinanoglu, Alper; Noujeim, Marcel; Helvacioglu Yigit, Dilek; Baydemir, Canan

    2016-05-01

    For forensic age estimation, radiographic assessment of third molar mineralization is important between 14 and 21 years which coincides with the legal age in most countries. The spheno-occipital synchondrosis (SOS) is an important growth site during development, and its use for age estimation is beneficial when combined with other markers. In this study, we aimed to develop a regression model to estimate and narrow the age range based on the radiologic assessment of third molar and SOS in a Turkish subpopulation. Panoramic radiographs and cone beam CT scans of 349 subjects (182 males, 167 females) with age between 8 and 25 were evaluated. Four-stage system was used to evaluate the fusion degree of SOS, and Demirjian's eight stages of development for calcification for third molars. The Pearson correlation indicated a strong positive relationship between age and third molar calcification for both sexes (r = 0.850 for females, r = 0.839 for males, P < 0.001) and also between age and SOS fusion for females (r = 0.814), but a moderate relationship was found for males (r = 0.599), P < 0.001). Based on the results obtained, an age determination formula using these scores was established.

  15. The use of nonlinear regression analysis for integrating pollutant concentration measurements with atmospheric dispersion modeling for source term estimation

    International Nuclear Information System (INIS)

    Edwards, L.L.; Freis, R.P.; Peters, L.G.; Gudiksen, P.H.; Pitovranov, S.E.

    1993-01-01

    The accuracy associated with assessing the environmental consequences of an accidental release of radioactivity is highly dependent on the knowledge of the source term characteristics, which are generally poorly known. The development of an automated numerical technique that integrates the radiological measurements with atmospheric dispersion modeling for more accurate source term estimation is reported. Often, this process of parameter estimation is performed by an emergency response assessor, who takes an intelligent first guess at the model parameters, then, comparing the model results with whatever measurements are available, makes an intuitive, informed next guess of the model parameters. This process may be repeated any number of times until the assessor feels that the model results are reasonable in terms of the measured observations. A new approach, based on a nonlinear least-squares regression scheme coupled with the existing Atmospheric Release Advisory Capability three-dimensional atmospheric dispersion models, is to supplement the assessor's intuition with automated mathematical methods that do not significantly increase the response time of the existing predictive models. The viability of the approach is evaluated by estimation of the known SF 6 tracer release rates associated with the Mesoscale Atmospheric Transport Studies tracer experiments conducted at the Savannah River Laboratory during 1983. These 19 experiments resulted in 14 successful, separate tracer releases with sampling of the tracer plumes along the cross-plume arc situated ∼30 km from the release site

  16. Estimation of snowpack matching ground-truth data and MODIS satellite-based observations by using regression kriging

    Science.gov (United States)

    Juan Collados-Lara, Antonio; Pardo-Iguzquiza, Eulogio; Pulido-Velazquez, David

    2016-04-01

    The estimation of Snow Water Equivalent (SWE) is essential for an appropriate assessment of the available water resources in Alpine catchment. The hydrologic regime in these areas is dominated by the storage of water in the snowpack, which is discharged to rivers throughout the melt season. An accurate estimation of the resources will be necessary for an appropriate analysis of the system operation alternatives using basin scale management models. In order to obtain an appropriate estimation of the SWE we need to know the spatial distribution snowpack and snow density within the Snow Cover Area (SCA). Data for these snow variables can be extracted from in-situ point measurements and air-borne/space-borne remote sensing observations. Different interpolation and simulation techniques have been employed for the estimation of the cited variables. In this paper we propose to estimate snowpack from a reduced number of ground-truth data (1 or 2 campaigns per year with 23 observation point from 2000-2014) and MODIS satellite-based observations in the Sierra Nevada Mountain (Southern Spain). Regression based methodologies has been used to study snowpack distribution using different kind of explicative variables: geographic, topographic, climatic. 40 explicative variables were considered: the longitude, latitude, altitude, slope, eastness, northness, radiation, maximum upwind slope and some mathematical transformation of each of them [Ln(v), (v)^-1; (v)^2; (v)^0.5). Eight different structure of regression models have been tested (combining 1, 2, 3 or 4 explicative variables). Y=B0+B1Xi (1); Y=B0+B1XiXj (2); Y=B0+B1Xi+B2Xj (3); Y=B0+B1Xi+B2XjXl (4); Y=B0+B1XiXk+B2XjXl (5); Y=B0+B1Xi+B2Xj+B3Xl (6); Y=B0+B1Xi+B2Xj+B3XlXk (7); Y=B0+B1Xi+B2Xj+B3Xl+B4Xk (8). Where: Y is the snow depth; (Xi, Xj, Xl, Xk) are the prediction variables (any of the 40 variables); (B0, B1, B2, B3) are the coefficients to be estimated. The ground data are employed to calibrate the multiple regressions. In

  17. Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

    Science.gov (United States)

    Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

    2003-11-01

    Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

  18. Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate

    Science.gov (United States)

    Guermoui, Mawloud; Gairaa, Kacem; Rabehi, Abdelaziz; Djafer, Djelloul; Benkaciali, Said

    2018-06-01

    Accurate estimation of solar radiation is the major concern in renewable energy applications. Over the past few years, a lot of machine learning paradigms have been proposed in order to improve the estimation performances, mostly based on artificial neural networks, fuzzy logic, support vector machine and adaptive neuro-fuzzy inference system. The aim of this work is the prediction of the daily global solar radiation, received on a horizontal surface through the Gaussian process regression (GPR) methodology. A case study of Ghardaïa region (Algeria) has been used in order to validate the above methodology. In fact, several combinations have been tested; it was found that, GPR-model based on sunshine duration, minimum air temperature and relative humidity gives the best results in term of mean absolute bias error (MBE), root mean square error (RMSE), relative mean square error (rRMSE), and correlation coefficient ( r) . The obtained values of these indicators are 0.67 MJ/m2, 1.15 MJ/m2, 5.2%, and 98.42%, respectively.

  19. A statistical regression model for the estimation of acrylamide concentrations in French fries for excess lifetime cancer risk assessment.

    Science.gov (United States)

    Chen, Ming-Jen; Hsu, Hui-Tsung; Lin, Cheng-Li; Ju, Wei-Yuan

    2012-10-01

    Human exposure to acrylamide (AA) through consumption of French fries and other foods has been recognized as a potential health concern. Here, we used a statistical non-linear regression model, based on the two most influential factors, cooking temperature and time, to estimate AA concentrations in French fries. The R(2) of the predictive model is 0.83, suggesting the developed model was significant and valid. Based on French fry intake survey data conducted in this study and eight frying temperature-time schemes which can produce tasty and visually appealing French fries, the Monte Carlo simulation results showed that if AA concentration is higher than 168 ppb, the estimated cancer risk for adolescents aged 13-18 years in Taichung City would be already higher than the target excess lifetime cancer risk (ELCR), and that by taking into account this limited life span only. In order to reduce the cancer risk associated with AA intake, the AA levels in French fries might have to be reduced even further if the epidemiological observations are valid. Our mathematical model can serve as basis for further investigations on ELCR including different life stages and behavior and population groups. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Econometric Analysis of the Demand for Pulses in Sri Lanka: An Almost Ideal Estimation with a Censored Regression

    Directory of Open Access Journals (Sweden)

    Lokuge Dona Manori Nimanthika Lokuge

    2015-06-01

    Full Text Available Due to high prevalence of dietary diseases and malnutrition in Sri Lanka, it is essential to assess food consumption patterns. Because pulses are a major source of nutrients, this paper employed the Linear Approximation of the Almost Ideal Demand System (LA/AIDS to estimate price and expenditure elasticities for six types of pulses, by utilizing the Household Income and Expenditure Survey, 2006/07. The infrequency of purchases, a typical problem encountered in LA/AIDS estimation is circumvented by using a probit regression in the first stage, to capture the effect of demographic factors, in consumption choice. Results reveal that the buying decision of pulses is influenced by the sector (rural, urban and estate, household size, education level, presence of children, prevalence of blood pressure and diabetes. All pulses types except dhal are highly responsive to their own prices. Dhal is identified as the most prominent choice among all other alternatives and hence, it is distinguished as a necessity whereas, the rest show luxurious behavior, with the income. Because dhal is an import product, consumption choices of dhal may be severely affected by any action which exporting countries introduce, while rest of the pulses will be affected by both price and income oriented policies.

  1. A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

    Directory of Open Access Journals (Sweden)

    Abdolreza Yazdani-Chamzini

    2017-12-01

    Full Text Available Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1 artificial intelligence, (2 statistical methods, and (3 analytical methods. In this paper, the multivariate regression (MVR method, which is one of the most popular linear models, and the artificial neural network (ANN method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy.

  2. Regression models for estimating concentrations of atrazine plus deethylatrazine in shallow groundwater in agricultural areas of the United States

    Science.gov (United States)

    Stackelberg, Paul E.; Barbash, Jack E.; Gilliom, Robert J.; Stone, Wesley W.; Wolock, David M.

    2012-01-01

    Tobit regression models were developed to predict the summed concentration of atrazine [6-chloro-N-ethyl-N'-(1-methylethyl)-1,3,5-triazine-2,4-diamine] and its degradate deethylatrazine [6-chloro-N-(1-methylethyl)-1,3,5,-triazine-2,4-diamine] (DEA) in shallow groundwater underlying agricultural settings across the conterminous United States. The models were developed from atrazine and DEA concentrations in samples from 1298 wells and explanatory variables that represent the source of atrazine and various aspects of the transport and fate of atrazine and DEA in the subsurface. One advantage of these newly developed models over previous national regression models is that they predict concentrations (rather than detection frequency), which can be compared with water quality benchmarks. Model results indicate that variability in the concentration of atrazine residues (atrazine plus DEA) in groundwater underlying agricultural areas is more strongly controlled by the history of atrazine use in relation to the timing of recharge (groundwater age) than by processes that control the dispersion, adsorption, or degradation of these compounds in the saturated zone. Current (1990s) atrazine use was found to be a weak explanatory variable, perhaps because it does not represent the use of atrazine at the time of recharge of the sampled groundwater and because the likelihood that these compounds will reach the water table is affected by other factors operating within the unsaturated zone, such as soil characteristics, artificial drainage, and water movement. Results show that only about 5% of agricultural areas have greater than a 10% probability of exceeding the USEPA maximum contaminant level of 3.0 μg L-1. These models are not developed for regulatory purposes but rather can be used to (i) identify areas of potential concern, (ii) provide conservative estimates of the concentrations of atrazine residues in deeper potential drinking water supplies, and (iii) set priorities

  3. Prediction of Compressional Wave Velocity Using Regression and Neural Network Modeling and Estimation of Stress Orientation in Bokaro Coalfield, India

    Science.gov (United States)

    Paul, Suman; Ali, Muhammad; Chatterjee, Rima

    2018-01-01

    Velocity of compressional wave ( V P) of coal and non-coal lithology is predicted from five wells from the Bokaro coalfield (CF), India. Shear sonic travel time logs are not recorded for all wells under the study area. Shear wave velocity ( Vs) is available only for two wells: one from east and other from west Bokaro CF. The major lithologies of this CF are dominated by coal, shaly coal of Barakar formation. This paper focuses on the (a) relationship between Vp and Vs, (b) prediction of Vp using regression and neural network modeling and (c) estimation of maximum horizontal stress from image log. Coal characterizes with low acoustic impedance (AI) as compared to the overlying and underlying strata. The cross-plot between AI and Vp/ Vs is able to identify coal, shaly coal, shale and sandstone from wells in Bokaro CF. The relationship between Vp and Vs is obtained with excellent goodness of fit ( R 2) ranging from 0.90 to 0.93. Linear multiple regression and multi-layered feed-forward neural network (MLFN) models are developed for prediction Vp from two wells using four input log parameters: gamma ray, resistivity, bulk density and neutron porosity. Regression model predicted Vp shows poor fit (from R 2 = 0.28) to good fit ( R 2 = 0.79) with the observed velocity. MLFN model predicted Vp indicates satisfactory to good R2 values varying from 0.62 to 0.92 with the observed velocity. Maximum horizontal stress orientation from a well at west Bokaro CF is studied from Formation Micro-Imager (FMI) log. Breakouts and drilling-induced fractures (DIFs) are identified from the FMI log. Breakout length of 4.5 m is oriented towards N60°W whereas the orientation of DIFs for a cumulative length of 26.5 m is varying from N15°E to N35°E. The mean maximum horizontal stress in this CF is towards N28°E.

  4. A comparison on parameter-estimation methods in multiple regression analysis with existence of multicollinearity among independent variables

    Directory of Open Access Journals (Sweden)

    Hukharnsusatrue, A.

    2005-11-01

    Full Text Available The objective of this research is to compare multiple regression coefficients estimating methods with existence of multicollinearity among independent variables. The estimation methods are Ordinary Least Squares method (OLS, Restricted Least Squares method (RLS, Restricted Ridge Regression method (RRR and Restricted Liu method (RL when restrictions are true and restrictions are not true. The study used the Monte Carlo Simulation method. The experiment was repeated 1,000 times under each situation. The analyzed results of the data are demonstrated as follows. CASE 1: The restrictions are true. In all cases, RRR and RL methods have a smaller Average Mean Square Error (AMSE than OLS and RLS method, respectively. RRR method provides the smallest AMSE when the level of correlations is high and also provides the smallest AMSE for all level of correlations and all sample sizes when standard deviation is equal to 5. However, RL method provides the smallest AMSE when the level of correlations is low and middle, except in the case of standard deviation equal to 3, small sample sizes, RRR method provides the smallest AMSE.The AMSE varies with, most to least, respectively, level of correlations, standard deviation and number of independent variables but inversely with to sample size.CASE 2: The restrictions are not true.In all cases, RRR method provides the smallest AMSE, except in the case of standard deviation equal to 1 and error of restrictions equal to 5%, OLS method provides the smallest AMSE when the level of correlations is low or median and there is a large sample size, but the small sample sizes, RL method provides the smallest AMSE. In addition, when error of restrictions is increased, OLS method provides the smallest AMSE for all level, of correlations and all sample sizes, except when the level of correlations is high and sample sizes small. Moreover, the case OLS method provides the smallest AMSE, the most RLS method has a smaller AMSE than

  5. Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

    Science.gov (United States)

    Veerkamp, R F; Koenen, E P; De Jong, G

    2001-10-01

    Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.

  6. Use of different marker pre-selection methods based on single SNP regression in the estimation of Genomic-EBVs

    Directory of Open Access Journals (Sweden)

    Corrado Dimauro

    2010-01-01

    Full Text Available Two methods of SNPs pre-selection based on single marker regression for the estimation of genomic breeding values (G-EBVs were compared using simulated data provided by the XII QTL-MAS workshop: i Bonferroni correction of the significance threshold and ii Permutation test to obtain the reference distribution of the null hypothesis and identify significant markers at P<0.01 and P<0.001 significance thresholds. From the set of markers significant at P<0.001, random subsets of 50% and 25% markers were extracted, to evaluate the effect of further reducing the number of significant SNPs on G-EBV predictions. The Bonferroni correction method allowed the identification of 595 significant SNPs that gave the best G-EBV accuracies in prediction generations (82.80%. The permutation methods gave slightly lower G-EBV accuracies even if a larger number of SNPs resulted significant (2,053 and 1,352 for 0.01 and 0.001 significance thresholds, respectively. Interestingly, halving or dividing by four the number of SNPs significant at P<0.001 resulted in an only slightly decrease of G-EBV accuracies. The genetic structure of the simulated population with few QTL carrying large effects, might have favoured the Bonferroni method.

  7. Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework.

    Science.gov (United States)

    Akita, Yasuyuki; Baldasano, Jose M; Beelen, Rob; Cirach, Marta; de Hoogh, Kees; Hoek, Gerard; Nieuwenhuijsen, Mark; Serre, Marc L; de Nazelle, Audrey

    2014-04-15

    In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.

  8. The Use of Alternative Regression Methods in Social Sciences and the Comparison of Least Squares and M Estimation Methods in Terms of the Determination of Coefficient

    Science.gov (United States)

    Coskuntuncel, Orkun

    2013-01-01

    The purpose of this study is two-fold; the first aim being to show the effect of outliers on the widely used least squares regression estimator in social sciences. The second aim is to compare the classical method of least squares with the robust M-estimator using the "determination of coefficient" (R[superscript 2]). For this purpose,…

  9. Building vulnerability to hydro-geomorphic hazards: Estimating damage probability from qualitative vulnerability assessment using logistic regression

    Science.gov (United States)

    Ettinger, Susanne; Mounaud, Loïc; Magill, Christina; Yao-Lafourcade, Anne-Françoise; Thouret, Jean-Claude; Manville, Vern; Negulescu, Caterina; Zuccaro, Giulio; De Gregorio, Daniela; Nardone, Stefano; Uchuchoque, Juan Alexis Luque; Arguedas, Anita; Macedo, Luisa; Manrique Llerena, Nélida

    2016-10-01

    The focus of this study is an analysis of building vulnerability through investigating impacts from the 8 February 2013 flash flood event along the Avenida Venezuela channel in the city of Arequipa, Peru. On this day, 124.5 mm of rain fell within 3 h (monthly mean: 29.3 mm) triggering a flash flood that inundated at least 0.4 km2 of urban settlements along the channel, affecting more than 280 buildings, 23 of a total of 53 bridges (pedestrian, vehicle and railway), and leading to the partial collapse of sections of the main road, paralyzing central parts of the city for more than one week. This study assesses the aspects of building design and site specific environmental characteristics that render a building vulnerable by considering the example of a flash flood event in February 2013. A statistical methodology is developed that enables estimation of damage probability for buildings. The applied method uses observed inundation height as a hazard proxy in areas where more detailed hydrodynamic modeling data is not available. Building design and site-specific environmental conditions determine the physical vulnerability. The mathematical approach considers both physical vulnerability and hazard related parameters and helps to reduce uncertainty in the determination of descriptive parameters, parameter interdependency and respective contributions to damage. This study aims to (1) enable the estimation of damage probability for a certain hazard intensity, and (2) obtain data to visualize variations in damage susceptibility for buildings in flood prone areas. Data collection is based on a post-flood event field survey and the analysis of high (sub-metric) spatial resolution images (Pléiades 2012, 2013). An inventory of 30 city blocks was collated in a GIS database in order to estimate the physical vulnerability of buildings. As many as 1103 buildings were surveyed along the affected drainage and 898 buildings were included in the statistical analysis. Univariate and

  10. Reduced Rank Regression

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

  11. Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

    Directory of Open Access Journals (Sweden)

    C. I. Cho

    2016-05-01

    Full Text Available The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs, and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK, fat yield (FAT, protein yield (PROT, and solids-not-fat yield (SNF. The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP of the third to fifth order (L3–L5, fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order. The residual variances in the models were either homogeneous (HOM or heterogeneous (15 classes, HET15; 60 classes, HET60. A total of nine models (3 orders of polynomials×3 types of residual variance including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC and/or Schwarz Bayesian information criteria (BIC statistics to identify the model(s of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF and L4-HET15 (FAT, which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first

  12. Organising medication discontinuation

    DEFF Research Database (Denmark)

    Nixon, Michael; Kousgaard, Marius Brostrøm

    2016-01-01

    medication? Methods: Twenty four GPs were interviewed using a maximum variation sample strategy. Participant observations were done in three general practices, for one day each, totalling approximately 30 consultations. Results: The results show that different discontinuation cues (related to the type...... a medication, in agreement with the patients, from a professional perspective. Three research questions were examined in this study: when does medication discontinuation occur in general practice, how is discontinuing medication handled in the GP’s practice and how do GPs make decisions about discontinuing...

  13. Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

    Directory of Open Access Journals (Sweden)

    Stefanie M. Herrmann

    2013-10-01

    Full Text Available Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, the actual extent of farmed parkland and spatial patterns of tree cover are largely unknown. We used the rule-based predictive modeling tool Cubist® to estimate field tree cover in the west-central agricultural region of Senegal. A collection of rules and associated multiple linear regression models was constructed from (1 a reference dataset of percent tree cover derived from very high spatial resolution data (2 m Orbview as the dependent variable, and (2 ten years of 10-day 250 m Moderate Resolution Imaging Spectrometer (MODIS Normalized Difference Vegetation Index (NDVI composites and derived phenological metrics as independent variables. Correlation coefficients between modeled and reference percent tree cover of 0.88 and 0.77 were achieved for training and validation data respectively, with absolute mean errors of 1.07 and 1.03 percent tree cover. The resulting map shows a west-east gradient from high tree cover in the peri-urban areas of horticulture and arboriculture to low tree cover in the more sparsely populated eastern part of the study area. A comparison of current (2000s tree cover along this gradient with historic cover as seen on Corona images reveals dynamics of change but also areas of remarkable stability of field tree cover since 1968. The proposed modeling approach can help to identify locations of high and low tree cover in dryland environments and guide ground studies and management interventions aimed at promoting the integration of field trees in agricultural systems.

  14. Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

    Science.gov (United States)

    2017-03-23

    Logistic Regression to Estimate the Median Will-Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle, B.S...not the other. We are able to give logistic regression models to program managers that identify several program characteristics for either...considered acceptable. We recommend the use of our logistic models as a tool to manage a portfolio of programs in order to gain potential elusive

  15. Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: simulation and verification with in vivo data

    International Nuclear Information System (INIS)

    Fang, Yu-Hua; Kao, Tsair; Liu, Ren-Shyan; Wu, Liang-Chih

    2004-01-01

    A novel statistical method, namely Regression-Estimated Input Function (REIF), is proposed in this study for the purpose of non-invasive estimation of the input function for fluorine-18 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) quantitative analysis. We collected 44 patients who had undergone a blood sampling procedure during their FDG-PET scans. First, we generated tissue time-activity curves of the grey matter and the whole brain with a segmentation technique for every subject. Summations of different intervals of these two curves were used as a feature vector, which also included the net injection dose. Multiple linear regression analysis was then applied to find the correlation between the input function and the feature vector. After a simulation study with in vivo data, the data of 29 patients were applied to calculate the regression coefficients, which were then used to estimate the input functions of the other 15 subjects. Comparing the estimated input functions with the corresponding real input functions, the averaged error percentages of the area under the curve and the cerebral metabolic rate of glucose (CMRGlc) were 12.13±8.85 and 16.60±9.61, respectively. Regression analysis of the CMRGlc values derived from the real and estimated input functions revealed a high correlation (r=0.91). No significant difference was found between the real CMRGlc and that derived from our regression-estimated input function (Student's t test, P>0.05). The proposed REIF method demonstrated good abilities for input function and CMRGlc estimation, and represents a reliable replacement for the blood sampling procedures in FDG-PET quantification. (orig.)

  16. Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

    Science.gov (United States)

    Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

    2017-01-01

    Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

  17. Estimation of genotype X environment interactions, in a grassbased system, for milk yield, body condition score,and body weight using random regression models

    NARCIS (Netherlands)

    Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.

    2003-01-01

    (Co)variance components for milk yield, body condition score (BCS), body weight (BW), BCS change and BW change over different herd-year mean milk yields (HMY) and nutritional environments (concentrate feeding level, grazing severity and silage quality) were estimated using a random regression model.

  18. Output-Only Modal Parameter Recursive Estimation of Time-Varying Structures via a Kernel Ridge Regression FS-TARMA Approach

    Directory of Open Access Journals (Sweden)

    Zhi-Sai Ma

    2017-01-01

    Full Text Available Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA. A kernel ridge regression functional series TARMA (FS-TARMA recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a numerical three-degree-of-freedom time-varying structural system and a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudolinear regression FS-TARMA approach via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics in a recursive manner.

  19. Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression.

    Science.gov (United States)

    Martínez Gila, Diego Manuel; Cano Marchal, Pablo; Gómez Ortega, Juan; Gámez García, Javier

    2018-03-25

    Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO), the last being an indicator of top quality. The polyphenol content is related to EVOO organoleptic features, and different scientific works have studied the positive influence that these compounds have on human health. The works carried out in this paper are focused on studying relations between the polyphenol content in olive oil samples and its spectral response in the near infrared spectra. In this context, several acquisition parameters have been assessed to optimize the measurement process within the virgin olive oil production process. The best regression model reached a mean error value of 156.14 mg/kg in leave one out cross validation, and the higher regression coefficient was 0.81 through holdout validation.

  20. Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression

    Directory of Open Access Journals (Sweden)

    Diego Manuel Martínez Gila

    2018-03-01

    Full Text Available Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO, the last being an indicator of top quality. The polyphenol content is related to EVOO organoleptic features, and different scientific works have studied the positive influence that these compounds have on human health. The works carried out in this paper are focused on studying relations between the polyphenol content in olive oil samples and its spectral response in the near infrared spectra. In this context, several acquisition parameters have been assessed to optimize the measurement process within the virgin olive oil production process. The best regression model reached a mean error value of 156.14 mg/kg in leave one out cross validation, and the higher regression coefficient was 0.81 through holdout validation.

  1. Direct and regression methods do not give different estimates of digestible and metabolizable energy of wheat for pigs.

    Science.gov (United States)

    Bolarinwa, O A; Adeola, O

    2012-12-01

    Digestible and metabolizable energy contents of feed ingredients for pigs can be determined by direct or indirect methods. There are situations when only the indirect approach is suitable and the regression method is a robust indirect approach. This study was conducted to compare the direct and regression methods for determining the energy value of wheat for pigs. Twenty-four barrows with an average initial BW of 31 kg were assigned to 4 diets in a randomized complete block design. The 4 diets consisted of 969 g wheat/kg plus minerals and vitamins (sole wheat) for the direct method, corn (Zea mays)-soybean (Glycine max) meal reference diet (RD), RD + 300 g wheat/kg, and RD + 600 g wheat/kg. The 3 corn-soybean meal diets were used for the regression method and wheat replaced the energy-yielding ingredients, corn and soybean meal, so that the same ratio of corn and soybean meal across the experimental diets was maintained. The wheat used was analyzed to contain 883 g DM, 15.2 g N, and 3.94 Mcal GE/kg. Each diet was fed to 6 barrows in individual metabolism crates for a 5-d acclimation followed by a 5-d total but separate collection of feces and urine. The DE and ME for the sole wheat diet were 3.83 and 3.77 Mcal/kg DM, respectively. Because the sole wheat diet contained 969 g wheat/kg, these translate to 3.95 Mcal DE/kg DM and 3.89 Mcal ME/kg DM. The RD used for the regression approach yielded 4.00 Mcal DE and 3.91 Mcal ME/kg DM diet. Increasing levels of wheat in the RD linearly reduced (P direct method (3.95 and 3.89 Mcal/kg DM) did not differ (0.78 < P < 0.89) from those obtained using the regression method (3.96 and 3.88 Mcal/kg DM).

  2. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery

    International Nuclear Information System (INIS)

    Hu, Chao; Jain, Gaurav; Zhang, Puqiang; Schmidt, Craig; Gomadam, Parthasarathy; Gorka, Tom

    2014-01-01

    Highlights: • We develop a data-driven method for the battery capacity estimation. • Five charge-related features that are indicative of the capacity are defined. • The kNN regression model captures the dependency of the capacity on the features. • Results with 10 years’ continuous cycling data verify the effectiveness of the method. - Abstract: Reliability of lithium-ion (Li-ion) rechargeable batteries used in implantable medical devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, physicians, and patients. To ensure Li-ion batteries in these devices operate reliably, it is important to be able to assess the battery health condition by estimating the battery capacity over the life-time. This paper presents a data-driven method for estimating the capacity of Li-ion battery based on the charge voltage and current curves. The contributions of this paper are three-fold: (i) the definition of five characteristic features of the charge curves that are indicative of the capacity, (ii) the development of a non-linear kernel regression model, based on the k-nearest neighbor (kNN) regression, that captures the complex dependency of the capacity on the five features, and (iii) the adaptation of particle swarm optimization (PSO) to finding the optimal combination of feature weights for creating a kNN regression model that minimizes the cross validation (CV) error in the capacity estimation. Verification with 10 years’ continuous cycling data suggests that the proposed method is able to accurately estimate the capacity of Li-ion battery throughout the whole life-time

  3. Regression models to estimate real-time concentrations of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-07

    Science.gov (United States)

    Oden, Timothy D.; Asquith, William H.; Milburn, Matthew S.

    2009-01-01

    In December 2005, the U.S. Geological Survey in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (total coliform and Escherichia coli), atrazine, and suspended sediment at two U.S. Geological Survey streamflow-gaging stations upstream from Lake Houston near Houston (08068500 Spring Creek near Spring, Texas, and 08070200 East Fork San Jacinto River near New Caney, Texas). The data from the discrete water-quality samples collected during 2005-07, in conjunction with monitored real-time data already being collected - physical properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), streamflow, and rainfall - were used to develop regression models for predicting water-quality constituent concentrations for inflows to Lake Houston. Rainfall data were obtained from a rain gage monitored by Harris County Homeland Security and Emergency Management and colocated with the Spring Creek station. The leaps and bounds algorithm was used to find the best subsets of possible regression models (minimum residual sum of squares for a given number of variables). The potential explanatory or predictive variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, rainfall, and time (to account for seasonal variations inherent in some water-quality data). The response variables at each site were nitrite plus nitrate nitrogen, total phosphorus, organic carbon, Escherichia coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities as a means to estimate concentrations of the various constituents under investigation, with accompanying estimates of measurement uncertainty. Each regression equation can be used to estimate concentrations of a given constituent in real time. In conjunction with estimated concentrations, constituent loads were estimated by multiplying the

  4. Support vector regression model for the estimation of γ-ray buildup factors for multi-layer shields

    International Nuclear Information System (INIS)

    Trontl, Kresimir; Smuc, Tomislav; Pevec, Dubravko

    2007-01-01

    The accuracy of the point-kernel method, which is a widely used practical tool for γ-ray shielding calculations, strongly depends on the quality and accuracy of buildup factors used in the calculations. Although, buildup factors for single-layer shields comprised of a single material are well known, calculation of buildup factors for stratified shields, each layer comprised of different material or a combination of materials, represent a complex physical problem. Recently, a new compact mathematical model for multi-layer shield buildup factor representation has been suggested for embedding into point-kernel codes thus replacing traditionally generated complex mathematical expressions. The new regression model is based on support vector machines learning technique, which is an extension of Statistical Learning Theory. The paper gives complete description of the novel methodology with results pertaining to realistic engineering multi-layer shielding geometries. The results based on support vector regression machine learning confirm that this approach provides a framework for general, accurate and computationally acceptable multi-layer buildup factor model

  5. Use of instantaneous streamflow measurements to improve regression estimates of index flow for the summer month of lowest streamflow in Michigan

    Science.gov (United States)

    Holtschlag, David J.

    2011-01-01

    In Michigan, index flow Q50 is a streamflow characteristic defined as the minimum of median flows for July, August, and September. The state of Michigan uses index flow estimates to help regulate large (greater than 100,000 gallons per day) water withdrawals to prevent adverse effects on characteristic fish populations. At sites where long-term streamgages are located, index flows are computed directly from continuous streamflow records as GageQ50. In an earlier study, a multiple-regression equation was developed to estimate index flows IndxQ50 at ungaged sites. The index equation explains about 94 percent of the variability of index flows at 147 (index) streamgages by use of six explanatory variables describing soil type, aquifer transmissivity, land cover, and precipitation characteristics. This report extends the results of the previous study, by use of Monte Carlo simulations, to evaluate alternative flow estimators, DiscQ50, IntgQ50, SiteQ50, and AugmQ50. The Monte Carlo simulations treated each of the available index streamgages, in turn, as a miscellaneous site where streamflow conditions are described by one or more instantaneous measurements of flow. In the simulations, instantaneous flows were approximated by daily mean flows at the corresponding site. All estimators use information that can be obtained from instantaneous flow measurements and contemporaneous daily mean flow data from nearby long-term streamgages. The efficacy of these estimators was evaluated over a set of measurement intensities in which the number of simulated instantaneous flow measurements ranged from 1 to 100 at a site. The discrete measurement estimator DiscQ50 is based on a simple linear regression developed between information on daily mean flows at five or more streamgages near the miscellaneous site and their corresponding GageQ50 index flows. The regression relation then was used to compute a DiscQ50 estimate at the miscellaneous site by use of the simulated instantaneous flow

  6. Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics

    Science.gov (United States)

    Baker, Ronald J.; Chepiga, Mary M.; Cauller, Stephen J.

    2015-01-01

    Nitrate-concentration data are used in conjunction with land-use and land-cover data to estimate median nitrate concentrations in groundwater underlying the New Jersey (NJ) Highlands Region. Sources of data on nitrate in 19,670 groundwater samples are from the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the NJ Private Well Testing Act (PWTA).

  7. Identifying the factors underlying discontinuation of triptans.

    Science.gov (United States)

    Wells, Rebecca E; Markowitz, Shira Y; Baron, Eric P; Hentz, Joseph G; Kalidas, Kavita; Mathew, Paul G; Halker, Rashmi; Dodick, David W; Schwedt, Todd J

    2014-02-01

    To identify factors associated with triptan discontinuation among migraine patients. It is unclear why many migraine patients who are prescribed triptans discontinue this treatment. This study investigated correlates of triptan discontinuation with a focus on potentially modifiable factors to improve compliance. This multicenter cross-sectional survey (n = 276) was performed at US tertiary care headache clinics. Headache fellows who were members of the American Headache Society Headache Fellows Research Consortium recruited episodic and chronic migraine patients who were current triptan users (use within prior 3 months and for ≥1 year) or past triptan users (no use within 6 months; prior use within 2 years). Univariate analyses were first completed to compare current triptan users to past users for: migraine characteristics, other migraine treatments, triptan education, triptan efficacy, triptan side effects, type of prescribing provider, Migraine Disability Assessment (MIDAS) scores and Beck Depression Inventory (BDI) scores. Then, a multivariable logistic regression model was selected from all possible combinations of predictor variables to determine the factors that best correlated with triptan discontinuation. Compared with those still using triptans (n = 207), those who had discontinued use (n = 69) had higher rates of medication overuse (30 vs. 18%, P = .04) and were more likely to have ever used opioids for migraine treatment (57 vs. 38%, P = .006) as well as higher MIDAS (mean 63 vs. 37, P = .001) and BDI scores (mean 10.4 vs. 7.4, P = .009). Compared with discontinued users, current triptan users were more likely to have had their triptan prescribed by a specialist (neurologist, headache specialist, or pain specialist) (74 vs. 54%, P = .002) and were more likely to report headache resolution (53 vs. 14%, P  24 (2.6, [1.5, 4.6]), BDI >4 (2.5, [1.4, 4.5]), and a history of ever using opioids for migraine therapy (2.2, [1

  8. Evaluation of Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) for Water Quality Monitoring: A Case Study for the Estimation of Salinity

    Science.gov (United States)

    Nazeer, Majid; Bilal, Muhammad

    2018-04-01

    Landsat-5 Thematic Mapper (TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square (OLS) regression and Geographically Weighted Regression (GWR) based on in situ data of October 2009. Results show that the coefficient of determination ( R 2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher ( R 2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay (north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant (32 practical salinity units) towards the open sea.

  9. Sample Size Estimation for Negative Binomial Regression Comparing Rates of Recurrent Events with Unequal Follow-Up Time.

    Science.gov (United States)

    Tang, Yongqiang

    2015-01-01

    A sample size formula is derived for negative binomial regression for the analysis of recurrent events, in which subjects can have unequal follow-up time. We obtain sharp lower and upper bounds on the required size, which is easy to compute. The upper bound is generally only slightly larger than the required size, and hence can be used to approximate the sample size. The lower and upper size bounds can be decomposed into two terms. The first term relies on the mean number of events in each group, and the second term depends on two factors that measure, respectively, the extent of between-subject variability in event rates, and follow-up time. Simulation studies are conducted to assess the performance of the proposed method. An application of our formulae to a multiple sclerosis trial is provided.

  10. Estimating the Influence of Accident Related Factors on Motorcycle Fatal Accidents using Logistic Regression (Case Study: Denpasar-Bali

    Directory of Open Access Journals (Sweden)

    Wedagama D.M.P.

    2010-01-01

    Full Text Available In Denpasar the capital of Bali Province, motorcycle accident contributes to about 80% of total road accidents. Out of those motorcycle accidents, 32% are fatal accidents. This study investigates the influence of accident related factors on motorcycle fatal accidents in the city of Denpasar during period 2006-2008 using a logistic regression model. The study found that the fatality of collision with pedestrians and right angle accidents were respectively about 0.44 and 0.40 times lower than collision with other vehicles and accidents due to other factors. In contrast, the odds that a motorcycle accident will be fatal due to collision with heavy and light vehicles were 1.67 times more likely than with other motorcycles. Collision with pedestrians, right angle accidents, and heavy and light vehicles were respectively accounted for 31%, 29%, and 63% of motorcycle fatal accidents.

  11. Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

    Science.gov (United States)

    NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

    2017-08-01

    Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

  12. Regional regression equations for the estimation of selected monthly low-flow duration and frequency statistics at ungaged sites on streams in New Jersey

    Science.gov (United States)

    Watson, Kara M.; McHugh, Amy R.

    2014-01-01

    Regional regression equations were developed for estimating monthly flow-duration and monthly low-flow frequency statistics for ungaged streams in Coastal Plain and non-coastal regions of New Jersey for baseline and current land- and water-use conditions. The equations were developed to estimate 87 different streamflow statistics, which include the monthly 99-, 90-, 85-, 75-, 50-, and 25-percentile flow-durations of the minimum 1-day daily flow; the August–September 99-, 90-, and 75-percentile minimum 1-day daily flow; and the monthly 7-day, 10-year (M7D10Y) low-flow frequency. These 87 streamflow statistics were computed for 41 continuous-record streamflow-gaging stations (streamgages) with 20 or more years of record and 167 low-flow partial-record stations in New Jersey with 10 or more streamflow measurements. The regression analyses used to develop equations to estimate selected streamflow statistics were performed by testing the relation between flow-duration statistics and low-flow frequency statistics for 32 basin characteristics (physical characteristics, land use, surficial geology, and climate) at the 41 streamgages and 167 low-flow partial-record stations. The regression analyses determined drainage area, soil permeability, average April precipitation, average June precipitation, and percent storage (water bodies and wetlands) were the significant explanatory variables for estimating the selected flow-duration and low-flow frequency statistics. Streamflow estimates were computed for two land- and water-use conditions in New Jersey—land- and water-use during the baseline period of record (defined as the years a streamgage had little to no change in development and water use) and current land- and water-use conditions (1989–2008)—for each selected station using data collected through water year 2008. The baseline period of record is representative of a period when the basin was unaffected by change in development. The current period is

  13. Factors predicting successful discontinuation of continuous renal replacement therapy.

    Science.gov (United States)

    Katayama, S; Uchino, S; Uji, M; Ohnuma, T; Namba, Y; Kawarazaki, H; Toki, N; Takeda, K; Yasuda, H; Izawa, J; Tokuhira, N; Nagata, I

    2016-07-01

    This multicentre, retrospective observational study was conducted from January 2010 to December 2010 to determine the optimal time for discontinuing continuous renal replacement therapy (CRRT) by evaluating factors predictive of successful discontinuation in patients with acute kidney injury. Analysis was performed for patients after CRRT was discontinued because of renal function recovery. Patients were divided into two groups according to the success or failure of CRRT discontinuation. In multivariate logistic regression analysis, urine output at discontinuation, creatinine level and CRRT duration were found to be significant variables (area under the receiver operating characteristic curve for urine output, 0.814). In conclusion, we found that higher urine output, lower creatinine and shorter CRRT duration were significant factors to predict successful discontinuation of CRRT.

  14. Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework

    Directory of Open Access Journals (Sweden)

    Yaxiong Ma

    2018-03-01

    Full Text Available Housing is a key component of urban sustainability. The objective of this study was to assess the significance of key spatial determinants of median home price in towns in Massachusetts that impact sustainable growth. Our analysis investigates the presence or absence of spatial non-stationarity in the relationship between sustainable growth, measured in terms of the relationship between home values and various parameters including the amount of unprotected forest land, residential land, unemployment, education, vehicle ownership, accessibility to commuter rail stations, school district performance, and senior population. We use the standard geographically weighted regression (GWR and Mixed GWR models to analyze the effects of spatial non-stationarity. Mixed GWR performed better than GWR in terms of Akaike Information Criterion (AIC values. Our findings highlight the nature and spatial extent of the non-stationary vs. stationary qualities of key environmental and social determinants of median home price. Understanding the key determinants of housing values, such as valuation of green spaces, public school performance metrics, and proximity to public transport, enable towns to use different strategies of sustainable urban planning, while understanding urban housing determinants—such as unemployment and senior population—can help modify urban sustainable housing policies.

  15. Estimating daily surface NO2 concentrations from satellite data - a case study over Hong Kong using land use regression models

    Science.gov (United States)

    Anand, Jasdeep S.; Monks, Paul S.

    2017-07-01

    Land use regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed-effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during the period 2005-2015. In situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A, and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in situ data shows that the mixed-effects LUR model using OMI data has a high predictive power (adj. R2 = 0. 84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0. 11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

  16. Regression and direct methods do not give different estimates of digestible and metabolizable energy values of barley, sorghum, and wheat for pigs.

    Science.gov (United States)

    Bolarinwa, O A; Adeola, O

    2016-02-01

    Direct or indirect methods can be used to determine the DE and ME of feed ingredients for pigs. In situations when only the indirect approach is suitable, the regression method presents a robust indirect approach. Three experiments were conducted to compare the direct and regression methods for determining the DE and ME values of barley, sorghum, and wheat for pigs. In each experiment, 24 barrows with an average initial BW of 31, 32, and 33 kg were assigned to 4 diets in a randomized complete block design. The 4 diets consisted of 969 g barley, sorghum, or wheat/kg plus minerals and vitamins for the direct method; a corn-soybean meal reference diet (RD); the RD + 300 g barley, sorghum, or wheat/kg; and the RD + 600 g barley, sorghum, or wheat/kg. The 3 corn-soybean meal diets were used for the regression method. Each diet was fed to 6 barrows in individual metabolism crates for a 5-d acclimation followed by a 5-d period of total but separate collection of feces and urine in each experiment. Graded substitution of barley or wheat, but not sorghum, into the RD linearly reduced ( direct method-derived DE and ME for barley were 3,669 and 3,593 kcal/kg DM, respectively. The regressions of barley contribution to DE and ME in kilocalories against the quantity of barley DMI in kilograms generated 3,746 kcal DE/kg DM and 3,647 kcal ME/kg DM. The DE and ME for sorghum by the direct method were 4,097 and 4,042 kcal/kg DM, respectively; the corresponding regression-derived estimates were 4,145 and 4,066 kcal/kg DM. Using the direct method, energy values for wheat were 3,953 kcal DE/kg DM and 3,889 kcal ME/kg DM. The regressions of wheat contribution to DE and ME in kilocalories against the quantity of wheat DMI in kilograms generated 3,960 kcal DE/kg DM and 3,874 kcal ME/kg DM. The DE and ME of barley using the direct method were not different (0.3 direct method-derived DE and ME of sorghum were not different (0.5 direct method- and regression method-derived DE (3,953 and 3

  17. Estimating solid waste generation by hospitality industry during major festivals: A quantification model based on multiple regression.

    Science.gov (United States)

    Abdulredha, Muhammad; Al Khaddar, Rafid; Jordan, David; Kot, Patryk; Abdulridha, Ali; Hashim, Khalid

    2018-04-26

    Major-religious festivals hosted in the city of Kerbala, Iraq, annually generate large quantities of Municipal Solid Waste (MSW) which negatively impacts the environment and human health when poorly managed. The hospitality sector, specifically hotels, is one of the major sources of MSW generated during these festivals. Because it is essential to establish a proper waste management system for such festivals, accurate information regarding MSW generation is required. This study therefore investigated the rate of production of MSW from hotels in Kerbala during major festivals. A field questionnaire survey was conducted with 150 hotels during the Arba'een festival, one of the largest festivals in the world, attended by about 18 million participants, to identify how much MSW is produced and what features of hotels impact on this. Hotel managers responded to questions regarding features of the hotel such as size (Hs), expenditure (Hex), area (Ha) and number of staff (Hst). An on-site audit was also carried out with all participating hotels to estimate the mass of MSW generated from these hotels. The results indicate that MSW produced by hotels varies widely. In general, it was found that each hotel guest produces an estimated 0.89 kg of MSW per day. However, this figure varies according to the hotels' rating. Average rates of MSW production from one and four star hotels were 0.83 and 1.22 kg per guest per day, respectively. Statistically, it was found that the relationship between MSW production and hotel features can be modelled with an R 2 of 0.799, where the influence of hotel feature on MSW production followed the order Hs > Hex > Hst. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. Challenges Associated with Estimating Utility in Wet Age-Related Macular Degeneration: A Novel Regression Analysis to Capture the Bilateral Nature of the Disease.

    Science.gov (United States)

    Hodgson, Robert; Reason, Timothy; Trueman, David; Wickstead, Rose; Kusel, Jeanette; Jasilek, Adam; Claxton, Lindsay; Taylor, Matthew; Pulikottil-Jacob, Ruth

    2017-10-01

    The estimation of utility values for the economic evaluation of therapies for wet age-related macular degeneration (AMD) is a particular challenge. Previous economic models in wet AMD have been criticized for failing to capture the bilateral nature of wet AMD by modelling visual acuity (VA) and utility values associated with the better-seeing eye only. Here we present a de novo regression analysis using generalized estimating equations (GEE) applied to a previous dataset of time trade-off (TTO)-derived utility values from a sample of the UK population that wore contact lenses to simulate visual deterioration in wet AMD. This analysis allows utility values to be estimated as a function of VA in both the better-seeing eye (BSE) and worse-seeing eye (WSE). VAs in both the BSE and WSE were found to be statistically significant (p regression analysis provides a possible source of utility values to allow future economic models to capture the quality of life impact of changes in VA in both eyes. Novartis Pharmaceuticals UK Limited.

  19. Estimation of Genetic Parameters for First Lactation Monthly Test-day Milk Yields using Random Regression Test Day Model in Karan Fries Cattle

    Directory of Open Access Journals (Sweden)

    Ajay Singh

    2016-06-01

    Full Text Available A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM considering different order of Legendre polynomial for the additive genetic effect (4th order and the permanent environmental effect (5th order. Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11 to 0.99 (TD-4 and TD-5. The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields.

  20. Minimax Regression Quantiles

    DEFF Research Database (Denmark)

    Bache, Stefan Holst

    A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....

  1. Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

    Science.gov (United States)

    Shastri, Niket; Pathak, Kamlesh

    2018-05-01

    The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

  2. Clearness index in cloudy days estimated with meteorological information by multiple regression analysis; Kisho joho wo riyoshita kaiki bunseki ni yoru dontenbi no seiten shisu no suitei

    Energy Technology Data Exchange (ETDEWEB)

    Nakagawa, S [Maizuru National College of Technology, Kyoto (Japan); Kenmoku, Y; Sakakibara, T [Toyohashi University of Technology, Aichi (Japan); Kawamoto, T [Shizuoka University, Shizuoka (Japan). Faculty of Engineering

    1996-10-27

    Study is under way for a more accurate solar radiation quantity prediction for the enhancement of solar energy utilization efficiency. Utilizing the technique of roughly estimating the day`s clearness index from forecast weather, the forecast weather (constituted of weather conditions such as `clear,` `cloudy,` etc., and adverbs or adjectives such as `afterward,` `temporary,` and `intermittent`) has been quantified relative to the clearness index. This index is named the `weather index` for the purpose of this article. The error high in rate in the weather index relates to cloudy days, which means a weather index falling in 0.2-0.5. It has also been found that there is a high correlation between the clearness index and the north-south wind direction component. A multiple regression analysis has been carried out, under the circumstances, for the estimation of clearness index from the maximum temperature and the north-south wind direction component. As compared with estimation of the clearness index on the basis only of the weather index, estimation using the weather index and maximum temperature achieves a 3% improvement throughout the year. It has also been learned that estimation by use of the weather index and north-south wind direction component enables a 2% improvement for summer and a 5% or higher improvement for winter. 2 refs., 6 figs., 4 tabs.

  3. Airline loyalty (programs) across borders : A geographic discontinuity approach

    NARCIS (Netherlands)

    de Jong, Gerben; Behrens, Christiaan; van Ommeren, Jos

    2018-01-01

    We analyze brand loyalty advantages of national airlines in their domestic countries using geocoded data from a major international frequent flier program. We employ a geographic discontinuity design that estimates discontinuities in program activity at the national borders of the program's

  4. New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression

    Science.gov (United States)

    Ichii, Kazuhito; Ueyama, Masahito; Kondo, Masayuki; Saigusa, Nobuko; Kim, Joon; Alberto, Ma. Carmelita; Ardö, Jonas; Euskirchen, Eugénie S.; Kang, Minseok; Hirano, Takashi; Joiner, Joanna; Kobayashi, Hideki; Marchesini, Luca Belelli; Merbold, Lutz; Miyata, Akira; Saitoh, Taku M.; Takagi, Kentaro; Varlagin, Andrej; Bret-Harte, M. Syndonia; Kitamura, Kenzo; Kosugi, Yoshiko; Kotani, Ayumi; Kumar, Kireet; Li, Sheng-Gong; Machimura, Takashi; Matsuura, Yojiro; Mizoguchi, Yasuko; Ohta, Takeshi; Mukherjee, Sandipan; Yanagi, Yuji; Yasuda, Yukio; Zhang, Yiping; Zhao, Fenghua

    2017-04-01

    The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r2 = 0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2 = 1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

  5. System dynamics with interaction discontinuity

    CERN Document Server

    Luo, Albert C J

    2015-01-01

    This book describes system dynamics with discontinuity caused by system interactions and presents the theory of flow singularity and switchability at the boundary in discontinuous dynamical systems. Based on such a theory, the authors address dynamics and motion mechanism of engineering discontinuous systems due to interaction. Stability and bifurcations of fixed points in nonlinear discrete dynamical systems are presented, and mapping dynamics are developed for analytical predictions of periodic motions in engineering discontinuous dynamical systems. Ultimately, the book provides an alternative way to discuss the periodic and chaotic behaviors in discontinuous dynamical systems.

  6. Regression Phalanxes

    OpenAIRE

    Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.

    2017-01-01

    Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...

  7. The Efficiency of OLS Estimators of Structural Parameters in a Simple Linear Regression Model in the Calibration of the Averages Scheme

    Directory of Open Access Journals (Sweden)

    Kowal Robert

    2016-12-01

    Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Multiple areas of research function within its scope. One of the many fundamental questions in the model concerns proving the efficiency of the most commonly used OLS estimators and examining their properties. In the literature of the subject one can find taking back to this scope and certain solutions in that regard. Methodically, they are borrowed from the multiple regression model or also from a boundary partial model. Not everything, however, is here complete and consistent. In the paper a completely new scheme is proposed, based on the implementation of the Cauchy-Schwarz inequality in the arrangement of the constraint aggregated from calibrated appropriately secondary constraints of unbiasedness which in a result of choice the appropriate calibrator for each variable directly leads to showing this property. A separate range-is a matter of choice of such a calibrator. These deliberations, on account of the volume and kinds of the calibration, were divided into a few parts. In the one the efficiency of OLS estimators is proven in a mixed scheme of the calibration by averages, that is preliminary, and in the most basic frames of the proposed methodology. In these frames the future outlines and general premises constituting the base of more distant generalizations are being created.

  8. Empirical estimation of the grades of hearing impairment among industrial workers based on new artificial neural networks and classical regression methods.

    Science.gov (United States)

    Farhadian, Maryam; Aliabadi, Mohsen; Darvishi, Ebrahim

    2015-01-01

    Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from actual cases. This study aimed to analyze the potential of artificial neural networks and logistic regression techniques for estimation of hearing impairment among industrial workers. A total of 210 workers employed in a steel factory (in West of Iran) were selected, and their occupational exposure histories were analyzed. The hearing loss thresholds of the studied workers were determined using a calibrated audiometer. The personal noise exposures were also measured using a noise dosimeter in the workstations. Data obtained from five variables, which can influence the hearing loss, were used as input features, and the hearing loss thresholds were considered as target feature of the prediction methods. Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software. Based on the World Health Organization classification for the grades of hearing loss, 74.2% of the studied workers have normal hearing thresholds, 23.4% have slight hearing loss, and 2.4% have moderate hearing loss. The accuracy and kappa coefficient of the best developed neural networks for prediction of the grades of hearing loss were 88.6 and 66.30, respectively. The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively. Neural networks could provide more accurate predictions of the hearing loss than logistic regression. The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.

  9. A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines.

    Science.gov (United States)

    Markham, Francis; Young, Martin; Doran, Bruce; Sugden, Mark

    2017-05-23

    Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM) and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs) and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016. A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost. Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by the models (I 2  ≥ 0.97; R 2  ≤ 0.01). The

  10. A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines

    Directory of Open Access Journals (Sweden)

    Francis Markham

    2017-05-01

    Full Text Available Abstract Background Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016. Methods A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost. Results Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by

  11. Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11

    Science.gov (United States)

    Baldwin, Austin K.; Robertson, Dale M.; Saad, David A.; Magruder, Christopher

    2013-01-01

    In 2008, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study to develop regression models to estimate real-time concentrations and loads of chloride, suspended solids, phosphorus, and bacteria in streams near Milwaukee, Wisconsin. To collect monitoring data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee River drainage basin. The sensors continuously measured four potential explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Using the first year of data, regression models were developed to continuously estimate the response variables on the basis of the continuously measured explanatory variables. Those models were published in a previous report. In this report, those models are refined using 2 years of additional data, and the relative improvement in model predictability is discussed. In addition, a set of regression models is presented for a new site in the Menomonee River Basin, Underwood Creek at Wauwatosa. The refined models use the same explanatory variables as the original models. The chloride models all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity. Total suspended solids and total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and turbidity as explanatory variables. An analysis of covariance (ANCOVA), used to compare the coefficients in the original models to those in the refined models calibrated using all of the data, showed that only 3 of the 25 original models changed significantly. Root-mean-squared errors (RMSEs

  12. Estimating Dbh of Trees Employing Multiple Linear Regression of the best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines

    Science.gov (United States)

    Ibanez, C. A. G.; Carcellar, B. G., III; Paringit, E. C.; Argamosa, R. J. L.; Faelga, R. A. G.; Posilero, M. A. V.; Zaragosa, G. P.; Dimayacyac, N. A.

    2016-06-01

    Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike's Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett's Test for Spherecity (BTS).

  13. Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression.

    Science.gov (United States)

    Hu, L; Liang, M; Mouraux, A; Wise, R G; Hu, Y; Iannetti, G D

    2011-12-01

    Across-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLR(d)) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF+MLR(d) method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF+MLR(d) approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLR(d) effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF+MLR(d) can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli.

  14. First-line antiretroviral drug discontinuations in children.

    Directory of Open Access Journals (Sweden)

    Melony Fortuin-de Smidt

    Full Text Available There are a limited number of paediatric antiretroviral drug options. Characterising the long term safety and durability of different antiretrovirals in children is important to optimise management of HIV infected children and to determine the estimated need for alternative drugs in paediatric regimens. We describe first-line antiretroviral therapy (ART durability and reasons for discontinuations in children at two South African ART programmes, where lopinavir/ritonavir has been recommended for children <3 years old since 2004, and abacavir replaced stavudine as the preferred nucleoside reverse transcriptase inhibitor in 2010.We included children (<16 years at ART initiation who initiated ≥3 antiretrovirals between 2004-2014 with ≥1 follow-up visit on ART. We estimated the incidence of first antiretroviral discontinuation using Kaplan-Meier analysis. We determined the reasons for antiretroviral discontinuations using competing risks analysis. We used Cox regression to identify factors associated with treatment-limiting toxicity.We included 3579 children with median follow-up duration of 41 months (IQR 14-72. At ART initiation, median age was 44 months (IQR 13-89 and median CD4 percent was 15% (IQR 9-21%. At three and five years on ART, 72% and 26% of children respectively remained on their initial regimen. By five years on ART, the most common reasons for discontinuations were toxicity (32%, treatment failure (18%, treatment simplification (5%, drug interactions (3%, and other or unspecified reasons (18%. The incidences of treatment limiting toxicity were 50.6 (95% CI 46.2-55.4, 1.6 (0.5-4.8, 2.0 (1.2-3.3, and 1.3 (0.6-2.8 per 1000 patient years for stavudine, abacavir, efavirenz and lopinavir/ritonavir respectively.While stavudine was associated with a high risk of treatment-limiting toxicity, abacavir, lopinavir/ritonavir and efavirenz were well-tolerated. This supports the World Health Organization recommendation to replace stavudine with

  15. Quantile Regression Methods

    DEFF Research Database (Denmark)

    Fitzenberger, Bernd; Wilke, Ralf Andreas

    2015-01-01

    if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

  16. Comparison of several measure-correlate-predict models using support vector regression techniques to estimate wind power densities. A case study

    International Nuclear Information System (INIS)

    Díaz, Santiago; Carta, José A.; Matías, José M.

    2017-01-01

    Highlights: • Eight measure-correlate-predict (MCP) models used to estimate the wind power densities (WPDs) at a target site are compared. • Support vector regressions are used as the main prediction techniques in the proposed MCPs. • The most precise MCP uses two sub-models which predict wind speed and air density in an unlinked manner. • The most precise model allows to construct a bivariable (wind speed and air density) WPD probability density function. • MCP models trained to minimise wind speed prediction error do not minimise WPD prediction error. - Abstract: The long-term annual mean wind power density (WPD) is an important indicator of wind as a power source which is usually included in regional wind resource maps as useful prior information to identify potentially attractive sites for the installation of wind projects. In this paper, a comparison is made of eight proposed Measure-Correlate-Predict (MCP) models to estimate the WPDs at a target site. Seven of these models use the Support Vector Regression (SVR) and the eighth the Multiple Linear Regression (MLR) technique, which serves as a basis to compare the performance of the other models. In addition, a wrapper technique with 10-fold cross-validation has been used to select the optimal set of input features for the SVR and MLR models. Some of the eight models were trained to directly estimate the mean hourly WPDs at a target site. Others, however, were firstly trained to estimate the parameters on which the WPD depends (i.e. wind speed and air density) and then, using these parameters, the target site mean hourly WPDs. The explanatory features considered are different combinations of the mean hourly wind speeds, wind directions and air densities recorded in 2014 at ten weather stations in the Canary Archipelago (Spain). The conclusions that can be drawn from the study undertaken include the argument that the most accurate method for the long-term estimation of WPDs requires the execution of a

  17. Application of Fourier transform infrared spectroscopy and orthogonal projections to latent structures/partial least squares regression for estimation of procyanidins average degree of polymerisation.

    Science.gov (United States)

    Passos, Cláudia P; Cardoso, Susana M; Barros, António S; Silva, Carlos M; Coimbra, Manuel A

    2010-02-28

    Fourier transform infrared (FTIR) spectroscopy has being emphasised as a widespread technique in the quick assess of food components. In this work, procyanidins were extracted with methanol and acetone/water from the seeds of white and red grape varieties. A fractionation by graded methanol/chloroform precipitations allowed to obtain 26 samples that were characterised using thiolysis as pre-treatment followed by HPLC-UV and MS detection. The average degree of polymerisation (DPn) of the procyanidins in the samples ranged from 2 to 11 flavan-3-ol residues. FTIR spectroscopy within the wavenumbers region of 1800-700 cm(-1) allowed to build a partial least squares (PLS1) regression model with 8 latent variables (LVs) for the estimation of the DPn, giving a RMSECV of 11.7%, with a R(2) of 0.91 and a RMSEP of 2.58. The application of orthogonal projection to latent structures (O-PLS1) clarifies the interpretation of the regression model vectors. Moreover, the O-PLS procedure has removed 88% of non-correlated variations with the DPn, allowing to relate the increase of the absorbance peaks at 1203 and 1099 cm(-1) with the increase of the DPn due to the higher proportion of substitutions in the aromatic ring of the polymerised procyanidin molecules. Copyright 2009 Elsevier B.V. All rights reserved.

  18. Computed statistics at streamgages, and methods for estimating low-flow frequency statistics and development of regional regression equations for estimating low-flow frequency statistics at ungaged locations in Missouri

    Science.gov (United States)

    Southard, Rodney E.

    2013-01-01

    The weather and precipitation patterns in Missouri vary considerably from year to year. In 2008, the statewide average rainfall was 57.34 inches and in 2012, the statewide average rainfall was 30.64 inches. This variability in precipitation and resulting streamflow in Missouri underlies the necessity for water managers and users to have reliable streamflow statistics and a means to compute select statistics at ungaged locations for a better understanding of water availability. Knowledge of surface-water availability is dependent on the streamflow data that have been collected and analyzed by the U.S. Geological Survey for more than 100 years at approximately 350 streamgages throughout Missouri. The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, computed streamflow statistics at streamgages through the 2010 water year, defined periods of drought and defined methods to estimate streamflow statistics at ungaged locations, and developed regional regression equations to compute selected streamflow statistics at ungaged locations. Streamflow statistics and flow durations were computed for 532 streamgages in Missouri and in neighboring States of Missouri. For streamgages with more than 10 years of record, Kendall’s tau was computed to evaluate for trends in streamflow data. If trends were detected, the variable length method was used to define the period of no trend. Water years were removed from the dataset from the beginning of the record for a streamgage until no trend was detected. Low-flow frequency statistics were then computed for the entire period of record and for the period of no trend if 10 or more years of record were available for each analysis. Three methods are presented for computing selected streamflow statistics at ungaged locations. The first method uses power curve equations developed for 28 selected streams in Missouri and neighboring States that have multiple streamgages on the same streams. Statistical

  19. Prediction of the distillation temperatures of crude oils using ¹H NMR and support vector regression with estimated confidence intervals.

    Science.gov (United States)

    Filgueiras, Paulo R; Terra, Luciana A; Castro, Eustáquio V R; Oliveira, Lize M S L; Dias, Júlio C M; Poppi, Ronei J

    2015-09-01

    This paper aims to estimate the temperature equivalent to 10% (T10%), 50% (T50%) and 90% (T90%) of distilled volume in crude oils using (1)H NMR and support vector regression (SVR). Confidence intervals for the predicted values were calculated using a boosting-type ensemble method in a procedure called ensemble support vector regression (eSVR). The estimated confidence intervals obtained by eSVR were compared with previously accepted calculations from partial least squares (PLS) models and a boosting-type ensemble applied in the PLS method (ePLS). By using the proposed boosting strategy, it was possible to identify outliers in the T10% property dataset. The eSVR procedure improved the accuracy of the distillation temperature predictions in relation to standard PLS, ePLS and SVR. For T10%, a root mean square error of prediction (RMSEP) of 11.6°C was obtained in comparison with 15.6°C for PLS, 15.1°C for ePLS and 28.4°C for SVR. The RMSEPs for T50% were 24.2°C, 23.4°C, 22.8°C and 14.4°C for PLS, ePLS, SVR and eSVR, respectively. For T90%, the values of RMSEP were 39.0°C, 39.9°C and 39.9°C for PLS, ePLS, SVR and eSVR, respectively. The confidence intervals calculated by the proposed boosting methodology presented acceptable values for the three properties analyzed; however, they were lower than those calculated by the standard methodology for PLS. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Human dental age estimation using third molar developmental stages: does a Bayesian approach outperform regression models to discriminate between juveniles and adults?

    Science.gov (United States)

    Thevissen, P W; Fieuws, S; Willems, G

    2010-01-01

    Dental age estimation methods based on the radiologically detected third molar developmental stages are implemented in forensic age assessments to discriminate between juveniles and adults considering the judgment of young unaccompanied asylum seekers. Accurate and unbiased age estimates combined with appropriate quantified uncertainties are the required properties for accurate forensic reporting. In this study, a subset of 910 individuals uniformly distributed in age between 16 and 22 years was selected from an existing dataset collected by Gunst et al. containing 2,513 panoramic radiographs with known third molar developmental stages of Belgian Caucasian men and women. This subset was randomly split in a training set to develop a classical regression analysis and a Bayesian model for the multivariate distribution of the third molar developmental stages conditional on age and in a test set to assess the performance of both models. The aim of this study was to verify if the Bayesian approach differentiates the age of maturity more precisely and removes the bias, which disadvantages the systematically overestimated young individuals. The Bayesian model offers the discrimination of subjects being older than 18 years more appropriate and produces more meaningful prediction intervals but does not strongly outperform the classical approaches.

  1. Soil Moisture Estimation over Vegetated Agricultural Areas: Tigris Basin, Turkey from Radarsat-2 Data by Polarimetric Decomposition Models and a Generalized Regression Neural Network

    Directory of Open Access Journals (Sweden)

    Mehmet Siraç Özerdem

    2017-04-01

    Full Text Available Determining the soil moisture in agricultural fields is a significant parameter to use irrigation systems efficiently. In contrast to standard soil moisture measurements, good results might be acquired in a shorter time over large areas by remote sensing tools. In order to estimate the soil moisture over vegetated agricultural areas, a relationship between Radarsat-2 data and measured ground soil moistures was established by polarimetric decomposition models and a generalized regression neural network (GRNN. The experiments were executed over two agricultural sites on the Tigris Basin, Turkey. The study consists of four phases. In the first stage, Radarsat-2 data were acquired on different dates and in situ measurements were implemented simultaneously. In the second phase, the Radarsat-2 data were pre-processed and the GPS coordinates of the soil sample points were imported to this data. Then the standard sigma backscattering coefficients with the Freeman–Durden and H/A/α polarimetric decomposition models were employed for feature extraction and a feature vector with four sigma backscattering coefficients (σhh, σhv, σvh, and σvv and six polarimetric decomposition parameters (entropy, anisotropy, alpha angle, volume scattering, odd bounce, and double bounce were generated for each pattern. In the last stage, GRNN was used to estimate the regional soil moisture with the aid of feature vectors. The results indicated that radar is a strong remote sensing tool for soil moisture estimation, with mean absolute errors around 2.31 vol %, 2.11 vol %, and 2.10 vol % for Datasets 1–3, respectively; and 2.46 vol %, 2.70 vol %, 7.09 vol %, and 5.70 vol % on Datasets 1 & 2, 2 & 3, 1 & 3, and 1 & 2 & 3, respectively.

  2. Stabilities of MHD rotational discontinuities

    International Nuclear Information System (INIS)

    Wang, S.

    1984-11-01

    In this paper, the stabilities of MHD rotational discontinuities are analyzed. The results show that the rotational discontinuities in an incompressible magnetofluid are not always stable with respect to infinitesimal perturbation. The instability condition in a special case is obtained. (author)

  3. Discontinuity formulas for multiparticle amplitudes

    International Nuclear Information System (INIS)

    Stapp, H.P.

    1976-03-01

    It is shown how discontinuity formulas for multiparticle scattering amplitudes are derived from unitarity and analyticity. The assumed analyticity property is the normal analytic structure, which was shown to be equivalent to the space-time macrocausality condition. The discontinuity formulas to be derived are the basis of multi-particle fixed-t dispersion relations

  4. Autistic Regression

    Science.gov (United States)

    Matson, Johnny L.; Kozlowski, Alison M.

    2010-01-01

    Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…

  5. Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L. Grown under Three Water Regimes

    Directory of Open Access Journals (Sweden)

    Javier Hernandez

    2015-02-01

    Full Text Available Plant breeding based on grain yield (GY is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes of wheat (Triticum aestivum L. through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed, mild water stress (MWS; one irrigation event around booting and full irrigation (FI with mean GYs of 1655, 4739, and 7967 kg∙ha−1, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R2 increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

  6. Estimation of Constituent Concentrations, Loads, and Yields in Streams of Johnson County, Northeast Kansas, Using Continuous Water-Quality Monitoring and Regression Models, October 2002 through December 2006

    Science.gov (United States)

    Rasmussen, Teresa J.; Lee, Casey J.; Ziegler, Andrew C.

    2008-01-01

    Johnson County is one of the most rapidly developing counties in Kansas. Population growth and expanding urban land use affect the quality of county streams, which are important for human and environmental health, water supply, recreation, and aesthetic value. This report describes estimates of streamflow and constituent concentrations, loads, and yields in relation to watershed characteristics in five Johnson County streams using continuous in-stream sensor measurements. Specific conductance, pH, water temperature, turbidity, and dissolved oxygen were monitored in five watersheds from October 2002 through December 2006. These continuous data were used in conjunction with discrete water samples to develop regression models for continuously estimating concentrations of other constituents. Continuous regression-based concentrations were estimated for suspended sediment, total suspended solids, dissolved solids and selected major ions, nutrients (nitrogen and phosphorus species), and fecal-indicator bacteria. Continuous daily, monthly, seasonal, and annual loads were calculated from concentration estimates and streamflow. The data are used to describe differences in concentrations, loads, and yields and to explain these differences relative to watershed characteristics. Water quality at the five monitoring sites varied according to hydrologic conditions; contributing drainage area; land use (including degree of urbanization); relative contributions from point and nonpoint constituent sources; and human activity within each watershed. Dissolved oxygen (DO) concentrations were less than the Kansas aquatic-life-support criterion of 5.0 mg/L less than 10 percent of the time at all sites except Indian Creek, which had DO concentrations less than the criterion about 15 percent of the time. Concentrations of suspended sediment, chloride (winter only), indicator bacteria, and pesticides were substantially larger during periods of increased streamflow. Suspended

  7. Boosted beta regression.

    Directory of Open Access Journals (Sweden)

    Matthias Schmid

    Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

  8. Linear regression

    CERN Document Server

    Olive, David J

    2017-01-01

    This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

  9. SEPARATION PHENOMENA LOGISTIC REGRESSION

    Directory of Open Access Journals (Sweden)

    Ikaro Daniel de Carvalho Barreto

    2014-03-01

    Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.

  10. Seasonal variation of benzo(a)pyrene in the Spanish airborne PM10. Multivariate linear regression model applied to estimate BaP concentrations.

    Science.gov (United States)

    Callén, M S; López, J M; Mastral, A M

    2010-08-15

    The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography mass-spectrometry mass-spectrometry (GC-MS-MS). Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R(2)=0.817, PRESS/SSY=0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001-2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (Q(CV)(2)=0.813, PRESS/SSY=0.187) and with the maximal external prediction for the 2001-2002 campaign (Q(ext)(2)=0.679 and PRESS/SSY=0.321) versus the 2001-2004 campaign (Q(ext)(2)=0.551, PRESS/SSY=0.449). Copyright 2010 Elsevier B.V. All rights reserved.

  11. Seasonal variation of benzo(a)pyrene in the Spanish airborne PM10. Multivariate linear regression model applied to estimate BaP concentrations

    International Nuclear Information System (INIS)

    Callen, M.S.; Lopez, J.M.; Mastral, A.M.

    2010-01-01

    The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography mass-spectrometry mass-spectrometry (GC-MS-MS). Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R 2 = 0.817, PRESS/SSY = 0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001-2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (Q CV 2 =0.813, PRESS/SSY = 0.187) and with the maximal external prediction for the 2001-2002 campaign (Q ext 2 =0.679 and PRESS/SSY = 0.321) versus the 2001-2004 campaign (Q ext 2 =0.551, PRESS/SSY = 0.449).

  12. Combining Off-the-Job Productivity Regression Model with EPA’s NONROAD Model in Estimating CO2 Emissions from Bulldozer

    Directory of Open Access Journals (Sweden)

    Apif M. Hajji

    2017-09-01

    Full Text Available Heavy duty diesel (HDD construction equipment which includes bulldozer is important in infrastructure development. This equipment consumes large amount of diesel fuel and emits high level of carbon dioxide (CO2. The total emissions are dependent upon the fuel use, and the fuel use is dependent upon the productivity of the equipment. This paper proposes a methodology and tool for estimating CO2 emissions from bulldozer based on the productivity rate. The methodology is formulated by using the result of multiple linear regressions (MLR of CAT’s data for obtaining the productivity model and combined with the EPA’s NONROAD model. The emission factors from NONROAD model were used to quantify the CO2 emissions. To display the function of the model, a case study and sensitivity analysis for a bulldozer’s activity is also presented. MLR results indicate that the productivity model generated from CAT’s data can be used as the basis for quantifying the total CO2 emissions for an earthwork activity.

  13. Extreme interplanetary rotational discontinuities at 1 AU

    Science.gov (United States)

    Lepping, R. P.; Wu, C.-C.

    2005-11-01

    This study is concerned with the identification and description of a special subset of four Wind interplanetary rotational discontinuities (from an earlier study of 134 directional discontinuities by Lepping et al. (2003)) with some "extreme" characteristics, in the sense that every case has (1) an almost planar current sheet surface, (2) a very large discontinuity angle (ω), (3) at least moderately strong normal field components (>0.8 nT), and (4) the overall set has a very broad range of transition layer thicknesses, with one being as thick as 50 RE and another at the other extreme being 1.6 RE, most being much thicker than are usually studied. Each example has a well-determined surface normal (n) according to minimum variance analysis and corroborated via time delay checking of the discontinuity with observations at IMP 8 by employing the local surface planarity. From the variance analyses, most of these cases had unusually large ratios of intermediate-to-minimum eigenvalues (λI/λmin), being on average 32 for three cases (with a fourth being much larger), indicating compact current sheet transition zones, another (the fifth) extreme property. For many years there has been a controversy as to the relative distribution of rotational (RDs) to tangential discontinuities (TDs) in the solar wind at 1 AU (and elsewhere, such as between the Sun and Earth), even to the point where some authors have suggested that RDs with large ∣Bn∣s are probably not generated or, if generated, are unstable and therefore very rare. Some of this disagreement apparently has been due to the different selection criteria used, e.g., some allowed eigenvalue ratios (λI/λmin) to be almost an order of magnitude lower than 32 in estimating n, usually introducing unacceptable error in n and therefore also in ∣Bn∣. However, we suggest that RDs may not be so rare at 1 AU, but good quality cases (where ∣Bn∣ confidently exceeds the error in ∣Bn∣) appear to be uncommon, and further

  14. Management applications of discontinuity theory

    Science.gov (United States)

    Angeler, David G.; Allen, Craig R.; Barichievy, Chris; Eason, Tarsha; Garmestani, Ahjond S.; Graham, Nicholas A.J.; Granholm, Dean; Gunderson, Lance H.; Knutson, Melinda; Nash, Kirsty L.; Nelson, R. John; Nystrom, Magnus; Spanbauer, Trisha; Stow, Craig A.; Sundstrom, Shana M.

    2015-01-01

    Human impacts on the environment are multifaceted and can occur across distinct spatiotemporal scales. Ecological responses to environmental change are therefore difficult to predict, and entail large degrees of uncertainty. Such uncertainty requires robust tools for management to sustain ecosystem goods and services and maintain resilient ecosystems.We propose an approach based on discontinuity theory that accounts for patterns and processes at distinct spatial and temporal scales, an inherent property of ecological systems. Discontinuity theory has not been applied in natural resource management and could therefore improve ecosystem management because it explicitly accounts for ecological complexity.Synthesis and applications. We highlight the application of discontinuity approaches for meeting management goals. Specifically, discontinuity approaches have significant potential to measure and thus understand the resilience of ecosystems, to objectively identify critical scales of space and time in ecological systems at which human impact might be most severe, to provide warning indicators of regime change, to help predict and understand biological invasions and extinctions and to focus monitoring efforts. Discontinuity theory can complement current approaches, providing a broader paradigm for ecological management and conservation.

  15. Discontinuities and the magnetospheric phenomena

    International Nuclear Information System (INIS)

    Rajaram, R.; Kalra, G.L.; Tandon, J.N.

    1978-01-01

    Wave coupling at contact discontinuities has an important bearing on the transmission of waves from the solar wind into the magnetosphere across the cusp region of the solar wind-magnetosphere boundary and on the propagation of geomagnetic pulsations in the polar exosphere. Keeping this in view, the problems of wave coupling across a contact discontinuity in a collisionless plasma, described by a set of double adiabatic fluid equations, is examined. The magnetic field is taken normal to the interface and it is shown that total reflection is not possible for any angle of incidence. The Alfven and the magneto-acoustic waves are not coupled. The transmission is most efficient for small density discontinuities. Inhibition of the transmission of the Alfven wave by the sharp density gradients above the F2-peak in the polar exosphere appears to account for the decrease in the pulsation amplitude, on the ground, as the poles are approached from the auroral zone. (author)

  16. The structure of rotational discontinuities

    International Nuclear Information System (INIS)

    Neugebauer, M.

    1989-01-01

    This study examines the structures of a set of rotational discontinuities detected in the solar wind by the ISEE-3 spacecraft. It is found that the complexity of the structure increases as the angle θ between the propagation vector k and the magnetic field decreases. For rotational discontinuities that propagate at a large angle to the field with an ion (left-hand) sense of rotation, the magnetic hodograms tend to be flattened, in agreement with prior numerical simulations. When θ is large, angular overshoots are often observed at one or both ends of the discontinuity. When the propagation is nearly parallel to the field (i.e., when θ is small), many different types of structure are seen, ranging from straight lines, the S-shaped curves, to complex, disorganized shapes

  17. Ridge Regression Signal Processing

    Science.gov (United States)

    Kuhl, Mark R.

    1990-01-01

    The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.

  18. Vector regression introduced

    Directory of Open Access Journals (Sweden)

    Mok Tik

    2014-06-01

    Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

  19. riskRegression

    DEFF Research Database (Denmark)

    Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas

    2017-01-01

    In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....

  20. Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area.

    NARCIS (Netherlands)

    Beelen, R.M.J.; Voogt, M.; Duyzer, J.; Zandveld, P.; Hoek, G.

    2010-01-01

    The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS - URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO2) concentrations for 2001 were estimated for nearly 70 000 centroids of a

  1. Regge calculus from discontinuous metrics

    International Nuclear Information System (INIS)

    Khatsymovsky, V.M.

    2003-01-01

    Regge calculus is considered as a particular case of the more general system where the linklengths of any two neighbouring 4-tetrahedra do not necessarily coincide on their common face. This system is treated as that one described by metric discontinuous on the faces. In the superspace of all discontinuous metrics the Regge calculus metrics form some hypersurface defined by continuity conditions. Quantum theory of the discontinuous metric system is assumed to be fixed somehow in the form of quantum measure on (the space of functionals on) the superspace. The problem of reducing this measure to the Regge hypersurface is addressed. The quantum Regge calculus measure is defined from a discontinuous metric measure by inserting the δ-function-like phase factor. The requirement that continuity conditions be imposed in a 'face-independent' way fixes this factor uniquely. The term 'face-independent' means that this factor depends only on the (hyper)plane spanned by the face, not on it's form and size. This requirement seems to be natural from the viewpoint of existence of the well-defined continuum limit maximally free of lattice artefacts

  2. Regression model development and computational procedures to support estimation of real-time concentrations and loads of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-9

    Science.gov (United States)

    Lee, Michael T.; Asquith, William H.; Oden, Timothy D.

    2012-01-01

    In December 2005, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (Escherichia coli and total coliform), atrazine, and suspended sediment at two USGS streamflow-gaging stations that represent watersheds contributing to Lake Houston (08068500 Spring Creek near Spring, Tex., and 08070200 East Fork San Jacinto River near New Caney, Tex.). Data from the discrete water-quality samples collected during 2005–9, in conjunction with continuously monitored real-time data that included streamflow and other physical water-quality properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), were used to develop regression models for the estimation of concentrations of water-quality constituents of substantial source watersheds to Lake Houston. The potential explanatory variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, and time (to account for seasonal variations inherent in some water-quality data). The response variables (the selected constituents) at each site were nitrite plus nitrate nitrogen, total phosphorus, total organic carbon, E. coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities to serve as potential surrogate variables to estimate concentrations of the selected constituents through statistical regression. Statistical regression also facilitates accompanying estimates of uncertainty in the form of prediction intervals. Each regression model potentially can be used to estimate concentrations of a given constituent in real time. Among other regression diagnostics, the diagnostics used as indicators of general model reliability and reported herein include the adjusted R-squared, the residual standard error, residual plots, and p-values. Adjusted R-squared values for the Spring Creek models ranged

  3. Polynomial regression analysis and significance test of the regression function

    International Nuclear Information System (INIS)

    Gao Zhengming; Zhao Juan; He Shengping

    2012-01-01

    In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

  4. Comparison of Neural Networks and Regression Time Series in Estimating the Development of the Afternoon Price of Palladium on the New York Stock Exchange

    Directory of Open Access Journals (Sweden)

    Marek Vochozka

    2017-12-01

    Full Text Available Purpose of the article: Palladium is presently used for producing electronics, industrial products or jewellery, as well as products in the medical field. Its value is raised especially by its unique physical and chemical characteristics. Predicting the value of such a metal is not an easy matter (with regard to the fact that prices may change significantly in time. Methodology/methods: To carry out the analysis, London Fix Price PM data was used, i.e. amounts reported in the afternoon for a period longer than 10 years. To process the data, Statistica software is used. Linear regression is carried out using a whole range of functions, and subsequently regression via neural structures is performed, where several distributional functions are used again. Subsequently, 1000 neural networks are generated, out of which 5 proving the best characteristics are chosen. Scientific aim: The aim of the paper is to perform a regression analysis of the development of the palladium price on the New York Stock Exchange using neural structures and linear regression, then to compare the two methods and determine the more suitable one for a possible prediction of the future development of the palladium price on the New York Stock Exchange. Findings: Results are compared on the level of an expert perspective and the evaluator’s – economist’s experience. Within regression time lines, the curve obtained by the least squares methods via negative-exponential smoothing gets closest to Palladium price line development. Out of the neural networks, all 5 chosen networks prove to be the most practically useful. Conclusions: Because it is not possible to predict extraordinary situations and their impact on the palladium price (at most in the short term, but certainly not over a long period of time, simplification and the creation of a relatively simple model is appropriate and the result is useful.

  5. Discontinuation of nicotine replacement therapy among smoking-cessation attempters.

    Science.gov (United States)

    Burns, Emily K; Levinson, Arnold H

    2008-03-01

    Nicotine replacement therapy (NRT) doubles successful quitting, but more than half of NRT users do not comply with optimal treatment regimens. From the 2005 Colorado state tobacco survey, quit attempters who utilized NRT (N=366) were analyzed in spring 2007. Descriptive and regression analyses were used to examine reasons for discontinuing NRT, length of time on NRT, and quit intentions. The reasons for discontinuing NRT were resuming smoking (34%), side effects (17%), NRT not helping with quitting (14%), quitting smoking (10%), and cost (5%). Poverty, age, and non-Latino minority status were associated with reasons for discontinuation other than quitting smoking. Having side effects was associated with a short duration of NRT use and 95% lower odds of intending to quit in the next month. In the first population-level study examining reasons for discontinuing NRT, general-population smokers who initiate NRT use when attempting to quit are highly likely to discontinue NRT prematurely. Age and culturally-appropriate medication management interventions may increase NRT compliance and improve cessation outcomes.

  6. A Seemingly Unrelated Poisson Regression Model

    OpenAIRE

    King, Gary

    1989-01-01

    This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.

  7. Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

    Science.gov (United States)

    Granato, Gregory E.

    2006-01-01

    The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and

  8. Human dental age estimation using third molar developmental stages: does a Bayesian approach outperform regression models to discriminate between juveniles and adults?

    OpenAIRE

    Thevissen, P. W.; FIEUWS, Steffen; Willems, G.

    2010-01-01

    Dental age estimation methods based on the radiologically detected third molar developmental stages are implemented in forensic age assessments to discriminate between juveniles and adults considering the judgment of young unaccompanied asylum seekers. Accurate and unbiased age estimates combined with appropriate quantified uncertainties are the required properties for accurate forensic reporting. In this study, a subset of 910 individuals uniformly distributed in age between 16 and 22 years ...

  9. Strategies for discontinuation of proton pump inhibitors

    DEFF Research Database (Denmark)

    Haastrup, Peter; Paulsen, Maja S; Begtrup, Luise M

    2014-01-01

    PURPOSE: Proton pump inhibitors (PPIs) are considered to be overprescribed. Consensus on how to attempt discontinuation is, however, lacking. We therefore conducted a systematic review of clinical studies on discontinuation of PPIs. METHODS: Systematic review based on clinical studies investigating...

  10. Factors affecting IUCD discontinuation in Nepal

    DEFF Research Database (Denmark)

    Thapa, Subash; Paudel, Ishwari Sharma; Bhattarai, Sailesh

    2015-01-01

    Information related to contraception discontinuation, especially in the context of Nepal is very limited. A nested case-control study was carried out to determine the factors affecting discontinuation of intrauterine contraceptive devices (IUCDs). A total of 115 cases (IUCD discontinuers) and 115...

  11. Linear regression in astronomy. I

    Science.gov (United States)

    Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh

    1990-01-01

    Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.

  12. Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

    NARCIS (Netherlands)

    Ramoelo, A.; Skidmore, A.K.; Cho, M.A.; Mathieu, R.; Heitkonig, I.M.A.; Dudeni-Tlhone, N.; Schlerf, M.; Prins, H.H.T.

    2013-01-01

    Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas

  13. Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

    CSIR Research Space (South Africa)

    Ramoelo, Abel

    2013-06-01

    Full Text Available in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only...

  14. Nonparametric Mixture of Regression Models.

    Science.gov (United States)

    Huang, Mian; Li, Runze; Wang, Shaoli

    2013-07-01

    Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.

  15. Bayesian ARTMAP for regression.

    Science.gov (United States)

    Sasu, L M; Andonie, R

    2013-10-01

    Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. Predictors of discontinuation of antipsychotic medication and subsequent outcomes in the European First Episode Schizophrenia Trial (EUFEST).

    Science.gov (United States)

    Landolt, Karin; Rössler, Wulf; Ajdacic-Gross, Vladeta; Derks, Eske M; Libiger, Jan; Kahn, René S; Fleischhacker, W Wolfgang

    2016-04-01

    This study had two aims: to describe patients suffering from first-episode schizophrenia who had stopped taking any antipsychotic medication, and to gain information on the predictors of successful discontinuation. We investigated data from the European First Episode Schizophrenia Trial (EUFEST). From the 325 patients included, 15.7% discontinued all antipsychotic medication. In a first analysis, clinical and sociodemographical predictors of discontinuing any antipsychotic medication were identified, using Cox regression. In the second analysis, logistic regression was used to determine variables associated with those patients who had stopped taking antipsychotic medication and had a favourable outcome, i.e., successful discontinuation. A good outcome was defined as a) having had no relapse within the whole observation period (80.6%), and b) having had no relapse and symptomatic remission at 12-month-follow-up (37.2%). Cox regression revealed that a higher proportion of patients from Western European countries and Israel stopped antipsychotic medication than from Central and Eastern European countries, that relapse was associated with discontinuation, and that discontinuers had lower compliance and higher quality of life. Predictors of successful discontinuation differed with the outcome definition used. Using definition b), successful discontinuers had a better baseline prognosis and better baseline social integration. Using definition a), successful discontinuers more often were from Western European countries. Region and clinical factors were associated with discontinuation. Prognosis and social integration played an important role in predicting successful discontinuation. As this study had several limitations, for example the observational design regarding discontinuation, further studies are needed to identify predictors of successful discontinuation. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Stability Analysis of Discontinuous Galerkin Approximations to the Elastodynamics Problem

    KAUST Repository

    Antonietti, Paola F.

    2015-11-21

    We consider semi-discrete discontinuous Galerkin approximations of both displacement and displacement-stress formulations of the elastodynamics problem. We prove the stability analysis in the natural energy norm and derive optimal a-priori error estimates. For the displacement-stress formulation, schemes preserving the total energy of the system are introduced and discussed. We verify our theoretical estimates on two and three dimensions test problems.

  18. Stability Analysis of Discontinuous Galerkin Approximations to the Elastodynamics Problem

    KAUST Repository

    Antonietti, Paola F.; Ayuso de Dios, Blanca; Mazzieri, Ilario; Quarteroni, Alfio

    2015-01-01

    We consider semi-discrete discontinuous Galerkin approximations of both displacement and displacement-stress formulations of the elastodynamics problem. We prove the stability analysis in the natural energy norm and derive optimal a-priori error estimates. For the displacement-stress formulation, schemes preserving the total energy of the system are introduced and discussed. We verify our theoretical estimates on two and three dimensions test problems.

  19. Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis

    Directory of Open Access Journals (Sweden)

    Hossein Mojaddadi Rizeei

    2018-01-01

    Full Text Available The current study proposes a new method for oil palm age estimation and counting. A support vector machine algorithm (SVM of object-based image analysis (OBIA was implemented for oil palm counting. It was integrated with height model and multiregression methods to accurately estimate the age of trees based on their heights in five different plantation blocks. Multiregression and multi-kernel size models were examined over five different oil palm plantation blocks to achieve the most optimized model for age estimation. The sensitivity analysis was conducted on four SVM kernel types (sigmoid (SIG, linear (LN, radial basis function (RBF, and polynomial (PL with associated parameters (threshold values, gamma γ, and penalty factor (c to obtain the optimal OBIA classification approaches for each plantation block. Very high-resolution imageries of WorldView-3 (WV-3 and light detection and range (LiDAR were used for oil palm detection and age assessment. The results of oil palm detection had an overall accuracy of 98.27%, 99.48%, 99.28%, 99.49%, and 97.49% for blocks A, B, C, D, and E, respectively. Moreover, the accuracy of age estimation analysis showed 90.1% for 3-year-old, 87.9% for 4-year-old, 88.0% for 6-year-old, 87.6% for 8-year-old, 79.1% for 9-year-old, and 76.8% for 22-year-old trees. Overall, the study revealed that remote sensing techniques can be useful to monitor and detect oil palm plantation for sustainable agricultural management.

  20. On the stability of rotational discontinuities

    International Nuclear Information System (INIS)

    Richter, P.; Scholer, M.

    1989-01-01

    The stability of symmetric rotational discontinuities in which the magnetic field rotates by 180 degree is investigated by means of a one-dimensional self-consistent hybrid code. Rotational discontinuities with an angle Θ > 45 degree between the discontinuity normal direction and the upstream magnetic field are found to be relatively stable. The discontinuity normal is in the x direction and the initial magnetic field has finite y component only in the transition region. In the case of the ion (left-handed) sense of rotation of the tangential magnetic field, the transition region does not broaden with time. In the case of the electron (right-handed) sense of rotation, a damped wavetrain builds up in the B y component downstream of the rotational discontinuity and the discontinuity broadens with time. Rotational discontinuities with smaller angles, Θ, are unstable. Examples for a rotational discontinuity with Θ = 30 degree and the electron sense of rotation as well as a rotational discontinuity with Θ = 15 degree and the ion sense of rotation show that these discontinuities into waves. These waves travel approximately with Alfven velocity in the upstream direction and are therefore phase standing in the simulation system. The magnetic hodograms of these disintegrated discontinuities are S-shaped. The upstream portion of the hodogram is always right-handed; the downstream portion is always left-handed

  1. Subset selection in regression

    CERN Document Server

    Miller, Alan

    2002-01-01

    Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...

  2. General Practitioners’ Decisions about Discontinuation of Medication

    DEFF Research Database (Denmark)

    Nixon, Michael Simon; Vendelø, Morten Thanning

    2016-01-01

    insights about decision making when discontinuing medication. It also offers one of the first examinations of how the institutional context embedding GPs influences their decisions about discontinuation. For policymakers interested in the discontinuation of medication, the findings suggest that de......Purpose – The purpose of this paper is to investigate how general practitioners’ (GPs) decisions about discontinuation of medication are influenced by their institutional context. Design/methodology/approach – In total, 24 GPs were interviewed, three practices were observed and documents were...... a weak frame for discontinuation. Three reasons for this are identified: the guidelines provide dominating triggers for prescribing, they provide weak priming for discontinuation as an option, and they underscore a cognitive constraint against discontinuation. Originality/value – The analysis offers new...

  3. Recent Developments on Discontinuous Precipitation

    Directory of Open Access Journals (Sweden)

    Zięba P.

    2017-06-01

    Full Text Available The discontinuous precipitation (DP belongs to a group of diffusive solid state phase transformations during which the formation of a new phase is heterogeneous and limited to a migrating reaction front (RF. The use of analytical electron microscopy provided reliable information that there is no differences in the diffusion rate at the stationary grain boundary and moving RF of DP reaction. On the other hand, the use of “in situ” transmission electron microscopy observations indicated the importance of stop-go motion or oscillatory movement of the RF.

  4. Differentiating regressed melanoma from regressed lichenoid keratosis.

    Science.gov (United States)

    Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

    2017-04-01

    Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  5. Quantile regression theory and applications

    CERN Document Server

    Davino, Cristina; Vistocco, Domenico

    2013-01-01

    A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and

  6. Seasonal variation of benzo(a) pyrene in the Spanish airborne PM10. Multivariate linear regression model applied to estimate BaP concentrations

    OpenAIRE

    Callén Romero, Mª Soledad; López Sebastián, José Manuel; Mastral Lamarca, Ana María

    2010-01-01

    The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography-mass spectrometry-mass spectrometry (...

  7. Steganalysis using logistic regression

    Science.gov (United States)

    Lubenko, Ivans; Ker, Andrew D.

    2011-02-01

    We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

  8. Adaptive metric kernel regression

    DEFF Research Database (Denmark)

    Goutte, Cyril; Larsen, Jan

    2000-01-01

    Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...

  9. Adaptive Metric Kernel Regression

    DEFF Research Database (Denmark)

    Goutte, Cyril; Larsen, Jan

    1998-01-01

    Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...

  10. Rock discontinuity surface roughness variation with scale

    Science.gov (United States)

    Bitenc, Maja; Kieffer, D. Scott; Khoshelham, Kourosh

    2017-04-01

    ABSTRACT: Rock discontinuity surface roughness refers to local departures of the discontinuity surface from planarity and is an important factor influencing the shear resistance. In practice, the Joint Roughness Coefficient (JRC) roughness parameter is commonly relied upon and input to a shear strength criterion such as developed by Barton and Choubey [1977]. The estimation of roughness by JRC is hindered firstly by the subjective nature of visually comparing the joint profile to the ten standard profiles. Secondly, when correlating the standard JRC values and other objective measures of roughness, the roughness idealization is limited to a 2D profile of 10 cm length. With the advance of measuring technologies that provide accurate and high resolution 3D data of surface topography on different scales, new 3D roughness parameters have been developed. A desirable parameter is one that describes rock surface geometry as well as the direction and scale dependency of roughness. In this research a 3D roughness parameter developed by Grasselli [2001] and adapted by Tatone and Grasselli [2009] is adopted. It characterizes surface topography as the cumulative distribution of local apparent inclination of asperities with respect to the shear strength (analysis) direction. Thus, the 3D roughness parameter describes the roughness amplitude and anisotropy (direction dependency), but does not capture the scale properties. In different studies the roughness scale-dependency has been attributed to data resolution or size of the surface joint (see a summary of researches in [Tatone and Grasselli, 2012]). Clearly, the lower resolution results in lower roughness. On the other hand, have the investigations of surface size effect produced conflicting results. While some studies have shown a decrease in roughness with increasing discontinuity size (negative scale effect), others have shown the existence of positive scale effects, or both positive and negative scale effects. We

  11. Casein Supplementation Does Not Affect the Estimates of True Total Tract Digestibility of Phosphorus in Soybean Meal for Growing Pigs Determined by the Regression Method

    Directory of Open Access Journals (Sweden)

    J. B. Liu

    2016-11-01

    Full Text Available Forty-eight barrows with an average initial body weight of 25.5±0.3 kg were assigned to 6 dietary treatments arranged in a 3×2 factorial of 3 graded levels of P at 1.42, 2.07, or 2.72 g/kg, and 2 levels of casein at 0 or 50 g/kg to compare the estimates of true total tract digestibility (TTTD of P in soybean meal (SBM for pigs fed diets with or without casein supplementation. The SBM is the only source of P in diets without casein, and in the diet with added casein, 1.0 to 2.4 g/kg of total dietary P was supplied by SBM as dietary level of SBM increased. The experiment consisted of a 5-d adjustment period and a 5-d total collection period with ferric oxide as a maker to indicate the initiation and termination of fecal collection. There were interactive effects of casein supplementation and total dietary P level on the apparent total tract digestibility (ATTD and retention of P (p0.05. In summary, our results demonstrate that the estimates of TTTD of P in SBM for pigs were not affected by constant casein inclusion in the basal diets.

  12. Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method for the parameter estimation on geographically weighted ordinal logistic regression model (GWOLR)

    Science.gov (United States)

    Saputro, Dewi Retno Sari; Widyaningsih, Purnami

    2017-08-01

    In general, the parameter estimation of GWOLR model uses maximum likelihood method, but it constructs a system of nonlinear equations, making it difficult to find the solution. Therefore, an approximate solution is needed. There are two popular numerical methods: the methods of Newton and Quasi-Newton (QN). Newton's method requires large-scale time in executing the computation program since it contains Jacobian matrix (derivative). QN method overcomes the drawback of Newton's method by substituting derivative computation into a function of direct computation. The QN method uses Hessian matrix approach which contains Davidon-Fletcher-Powell (DFP) formula. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is categorized as the QN method which has the DFP formula attribute of having positive definite Hessian matrix. The BFGS method requires large memory in executing the program so another algorithm to decrease memory usage is needed, namely Low Memory BFGS (LBFGS). The purpose of this research is to compute the efficiency of the LBFGS method in the iterative and recursive computation of Hessian matrix and its inverse for the GWOLR parameter estimation. In reference to the research findings, we found out that the BFGS and LBFGS methods have arithmetic operation schemes, including O(n2) and O(nm).

  13. Rotational discontinuities in anisotropic plasmas

    International Nuclear Information System (INIS)

    Omidi, N.

    1992-01-01

    The kinetic structure of rotational discontinuities (RDs) in anisotropic plasmas with T perpendicular /T parallel > 1 is investigated by using a one-dimensional electromagnetic hybrid code. To form the RD, a new approach is used where the plasma is injected from one boundary and reflected from the other, resulting in the generation of a traveling fast shock and an RD. Unlike the previously used methods, no a priori assumptions are made regarding the initial structure (i.e. width or sense of rotation) of the rotational discontinuity. The results show that across the RD both the magnetic field strength and direction, as well as the plasma density change. Given that such a change can also be associated with an intermediate shock, the Rankine-Hugoniot relations are used to confirm that the observed structures are indeed RDs. It is found that the thickness of RDs is a few ion inertial lengths and is independent of the rotation angle. Also, the preferred sense of rotation is in the electron sense; however, RDs with a rotation angle larger than 180 degree are found to be unstable, changing their rotation to a stable ion sense

  14. Development of an Anisotropic Geological-Based Land Use Regression and Bayesian Maximum Entropy Model for Estimating Groundwater Radon across Northing Carolina

    Science.gov (United States)

    Messier, K. P.; Serre, M. L.

    2015-12-01

    Radon (222Rn) is a naturally occurring chemically inert, colorless, and odorless radioactive gas produced from the decay of uranium (238U), which is ubiquitous in rocks and soils worldwide. Exposure to 222Rn is likely the second leading cause of lung cancer after cigarette smoking via inhalation; however, exposure through untreated groundwater is also a contributing factor to both inhalation and ingestion routes. A land use regression (LUR) model for groundwater 222Rn with anisotropic geological and 238U based explanatory variables is developed, which helps elucidate the factors contributing to elevated 222Rn across North Carolina. Geological and uranium based variables are constructed in elliptical buffers surrounding each observation such that they capture the lateral geometric anisotropy present in groundwater 222Rn. Moreover, geological features are defined at three different geological spatial scales to allow the model to distinguish between large area and small area effects of geology on groundwater 222Rn. The LUR is also integrated into the Bayesian Maximum Entropy (BME) geostatistical framework to increase accuracy and produce a point-level LUR-BME model of groundwater 222Rn across North Carolina including prediction uncertainty. The LUR-BME model of groundwater 222Rn results in a leave-one out cross-validation of 0.46 (Pearson correlation coefficient= 0.68), effectively predicting within the spatial covariance range. Modeled results of 222Rn concentrations show variability among Intrusive Felsic geological formations likely due to average bedrock 238U defined on the basis of overlying stream-sediment 238U concentrations that is a widely distributed consistently analyzed point-source data.

  15. Regression: A Bibliography.

    Science.gov (United States)

    Pedrini, D. T.; Pedrini, Bonnie C.

    Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

  16. Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal

    Directory of Open Access Journals (Sweden)

    Mohamed Gar Alalm

    2018-05-01

    Full Text Available Chlorothalonil is a pesticide that can contaminate water bodies, detriment aquatic organisms, and cause cancers of the forestomach and kidney. In this study, a powdered activated carbon prepared from casuarina wood was used for the adsorption of chlorothalonil from aqueous solutions. Based on Scanning Electron microscopy and Fourier Transform Infrared Spectroscopy analyses, the adsorbent material comprised pores and multiple functional groups that favored the entrapment of chlorothalonil onto its surface. At initial chlorothalonil concentration of 480 mg L−1, the equilibrium uptake capacity was 187 mg g−1 at pH: 7, adsorbent dosage: 0.5 g L−1, contact time: 40 min, and room temperature (25 ± 4 °C. The kinetic and isotherm studies indicated that the rate constant of pseudo-second-order model (k2 was 0.003 g mg−1 min−1, and the monolayer adsorption capacity was 192 mg g−1. Results from a quadratic model demonstrated that the plot of adsorption capacity versus pH, chlorothalonil concentration, adsorbent dosage, and contact time caused quadratic-concave, linear-up, flat, and quadratic-linear concave up curves, respectively. An artificial neural network with a structure of 4–5–1 was able to predict the adsorption capacity (R2: 0.982, and the sensitivity analysis using connection weights showed that pH was the most influential factor. An economic estimation using amortization and operating costs revealed that an adsorption unit subjected to 100 m3 d−1 containing chlorothalonil concentration of 250 ± 50 mg L−1 could cost 1.18 $ m−3. Keywords: Activated carbon, Artificial neural network, Chlorothalonil pesticide, Cost estimation, Kinetics and isotherms

  17. Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North at Fargo and Grand Forks, North Dakota, 2003-12

    Science.gov (United States)

    Galloway, Joel M.

    2014-01-01

    The Red River of the North (hereafter referred to as “Red River”) Basin is an important hydrologic region where water is a valuable resource for the region’s economy. Continuous water-quality monitors have been operated by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, Minnesota Pollution Control Agency, City of Fargo, City of Moorhead, City of Grand Forks, and City of East Grand Forks at the Red River at Fargo, North Dakota, from 2003 through 2012 and at Grand Forks, N.Dak., from 2007 through 2012. The purpose of the monitoring was to provide a better understanding of the water-quality dynamics of the Red River and provide a way to track changes in water quality. Regression equations were developed that can be used to estimate concentrations and loads for dissolved solids, sulfate, chloride, nitrate plus nitrite, total phosphorus, and suspended sediment using explanatory variables such as streamflow, specific conductance, and turbidity. Specific conductance was determined to be a significant explanatory variable for estimating dissolved solids concentrations at the Red River at Fargo and Grand Forks. The regression equations provided good relations between dissolved solid concentrations and specific conductance for the Red River at Fargo and at Grand Forks, with adjusted coefficients of determination of 0.99 and 0.98, respectively. Specific conductance, log-transformed streamflow, and a seasonal component were statistically significant explanatory variables for estimating sulfate in the Red River at Fargo and Grand Forks. Regression equations provided good relations between sulfate concentrations and the explanatory variables, with adjusted coefficients of determination of 0.94 and 0.89, respectively. For the Red River at Fargo and Grand Forks, specific conductance, streamflow, and a seasonal component were statistically significant explanatory variables for estimating chloride. For the Red River at Grand Forks, a time

  18. Direct-on-Filter α-Quartz Estimation in Respirable Coal Mine Dust Using Transmission Fourier Transform Infrared Spectrometry and Partial Least Squares Regression.

    Science.gov (United States)

    Miller, Arthur L; Weakley, Andrew Todd; Griffiths, Peter R; Cauda, Emanuele G; Bayman, Sean

    2017-05-01

    In order to help reduce silicosis in miners, the National Institute for Occupational Health and Safety (NIOSH) is developing field-portable methods for measuring airborne respirable crystalline silica (RCS), specifically the polymorph α-quartz, in mine dusts. In this study we demonstrate the feasibility of end-of-shift measurement of α-quartz using a direct-on-filter (DoF) method to analyze coal mine dust samples deposited onto polyvinyl chloride filters. The DoF method is potentially amenable for on-site analyses, but deviates from the current regulatory determination of RCS for coal mines by eliminating two sample preparation steps: ashing the sampling filter and redepositing the ash prior to quantification by Fourier transform infrared (FT-IR) spectrometry. In this study, the FT-IR spectra of 66 coal dust samples from active mines were used, and the RCS was quantified by using: (1) an ordinary least squares (OLS) calibration approach that utilizes standard silica material as done in the Mine Safety and Health Administration's P7 method; and (2) a partial least squares (PLS) regression approach. Both were capable of accounting for kaolinite, which can confound the IR analysis of silica. The OLS method utilized analytical standards for silica calibration and kaolin correction, resulting in a good linear correlation with P7 results and minimal bias but with the accuracy limited by the presence of kaolinite. The PLS approach also produced predictions well-correlated to the P7 method, as well as better accuracy in RCS prediction, and no bias due to variable kaolinite mass. Besides decreased sensitivity to mineral or substrate confounders, PLS has the advantage that the analyst is not required to correct for the presence of kaolinite or background interferences related to the substrate, making the method potentially viable for automated RCS prediction in the field. This study demonstrated the efficacy of FT-IR transmission spectrometry for silica determination in

  19. Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data

    Science.gov (United States)

    Tong, Xuming; Chen, Jinghang; Miao, Hongyu; Li, Tingting; Zhang, Le

    2015-01-01

    Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data. PMID:26535589

  20. Organising medication discontinuation: a qualitative study exploring the views of general practitioners toward discontinuing statins.

    Science.gov (United States)

    Nixon, Michael; Kousgaard, Marius Brostrøm

    2016-07-07

    Discontinuing medications is a complex decision making process and an important medical practice. It is a tool in reducing polypharmacy, reducing health system expenditure and improving patient quality of life. Few studies have looked at how general practitioners (GPs) discontinue a medication, in agreement with the patients, from a professional perspective. Three research questions were examined in this study: when does medication discontinuation occur in general practice, how is discontinuing medication handled in the GP's practice and how do GPs make decisions about discontinuing medication? Twenty four GPs were interviewed using a maximum variation sample strategy. Participant observations were done in three general practices, for one day each, totalling approximately 30 consultations. The results show that different discontinuation cues (related to the type of consultation, medical records and the patient) create situations of dissonance that can lead to the GP considering the option of discontinuation. We also show that there is a lot of ambiguity in situations of discontinuing and that some GPs trialled discontinuing as means of generating more information that could be used to deal with the ambiguity. We conclude that the practice of discontinuation should be conceptualised as a continually evaluative process and one that requires sustained reflection through a culture of systematically scheduled check-ups, routinely eliciting the patient's experience of taking drugs and trialling discontinuation. Some policy recommendations are offered including supporting GPs with lists or handbooks that directly address discontinuation and by developing more person centred clinical guidelines that discuss discontinuation more explicitly.

  1. Unbalanced Regressions and the Predictive Equation

    DEFF Research Database (Denmark)

    Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

    Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...

  2. From Rasch scores to regression

    DEFF Research Database (Denmark)

    Christensen, Karl Bang

    2006-01-01

    Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....

  3. Predicted effect size of lisdexamfetamine treatment of attention deficit/hyperactivity disorder (ADHD) in European adults: Estimates based on indirect analysis using a systematic review and meta-regression analysis.

    Science.gov (United States)

    Fridman, M; Hodgkins, P S; Kahle, J S; Erder, M H

    2015-06-01

    There are few approved therapies for adults with attention-deficit/hyperactivity disorder (ADHD) in Europe. Lisdexamfetamine (LDX) is an effective treatment for ADHD; however, no clinical trials examining the efficacy of LDX specifically in European adults have been conducted. Therefore, to estimate the efficacy of LDX in European adults we performed a meta-regression of existing clinical data. A systematic review identified US- and Europe-based randomized efficacy trials of LDX, atomoxetine (ATX), or osmotic-release oral system methylphenidate (OROS-MPH) in children/adolescents and adults. A meta-regression model was then fitted to the published/calculated effect sizes (Cohen's d) using medication, geographical location, and age group as predictors. The LDX effect size in European adults was extrapolated from the fitted model. Sensitivity analyses performed included using adult-only studies and adding studies with placebo designs other than a standard pill-placebo design. Twenty-two of 2832 identified articles met inclusion criteria. The model-estimated effect size of LDX for European adults was 1.070 (95% confidence interval: 0.738, 1.401), larger than the 0.8 threshold for large effect sizes. The overall model fit was adequate (80%) and stable in the sensitivity analyses. This model predicts that LDX may have a large treatment effect size in European adults with ADHD. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  4. Risk of Vascular Thrombotic Events Following Discontinuation of Antithrombotics After Peptic Ulcer Bleeding.

    Science.gov (United States)

    Kim, Seung Young; Hyun, Jong Jin; Suh, Sang Jun; Jung, Sung Woo; Jung, Young Kul; Koo, Ja Seol; Yim, Hyung Joon; Park, Jong Jae; Chun, Hoon Jai; Lee, Sang Woo

    2016-04-01

    To evaluate whether the risk of cardiovascular events increases when antithrombotics are discontinued after ulcer bleeding. Peptic ulcer bleeding associated with antithrombotics has increased due to the increase in the proportion of elderly population. Little is known about the long-term effects of discontinuing antithrombotics after peptic ulcer bleeding. The aim of this study was to evaluate whether the risk of cardiovascular events increases when antithrombotics are discontinued after ulcer bleeding. We reviewed the medical records of patients with ulcer bleeding who were taking antiplatelet agents or anticoagulants at the time of ulcer bleeding. Cox-regression model was used to adjust for potential confounders, and analyzed association between discontinuation of antithrombotic drugs after ulcer bleeding and thrombotic events such as ischemic heart disease or stroke. Of the 544 patients with ulcer bleeding, 72 patients who were taking antithrombotics and followed up for >2 months were analyzed. Forty patients discontinued antithrombotics after ulcer bleeding (discontinuation group) and 32 patients continued antithrombotics with or without transient interruption (continuation group). Thrombotic events developed more often in discontinuation group than in the continuation group [7/32 (21.9%) vs. 1/40 (2.5%), P=0.019]. Hazard ratio for thrombotic event when antithrombotics were continuously discontinued was 10.9 (95% confidence interval, 1.3-89.7). There were no significant differences in recurrent bleeding events between the 2 groups. Discontinuation of antithrombotics after peptic ulcer bleeding increases the risk of cardiovascular events. Therefore, caution should be taken when discontinuing antithrombotics after ulcer bleeding.

  5. Discontinuities during UV writing of waveguides

    DEFF Research Database (Denmark)

    Svalgaard, Mikael; Harpøth, Anders; Andersen, Marc

    2005-01-01

    UV writing of waveguides can be hampered by discontinuities where the index change process suddenly shuts down. We show that thermal effects may account for this behaviour.......UV writing of waveguides can be hampered by discontinuities where the index change process suddenly shuts down. We show that thermal effects may account for this behaviour....

  6. Vertebral Fractures After Discontinuation of Denosumab

    DEFF Research Database (Denmark)

    Cummings, Steven R; Ferrari, Serge; Eastell, Richard

    2018-01-01

    . We analyzed the risk of new or worsening vertebral fractures, especially multiple vertebral fractures, in participants who discontinued denosumab during the FREEDOM study or its Extension. Participants received ≥2 doses of denosumab or placebo Q6M, discontinued treatment, and stayed in the study ≥7...... months after the last dose. Of 1001 participants who discontinued denosumab during FREEDOM or Extension, the vertebral fracture rate increased from 1.2 per 100 participant-years during the on-treatment period to 7.1, similar to participants who received and then discontinued placebo (n = 470; 8.5 per 100....... Therefore, patients who discontinue denosumab should rapidly transition to an alternative antiresorptive treatment. Clinicaltrails.gov: NCT00089791 (FREEDOM) and NCT00523341 (Extension). © 2017 American Society for Bone and Mineral Research....

  7. Green's function approach to neutron flux discontinuities

    International Nuclear Information System (INIS)

    Saad, E.A.; El-Wakil, S.A.

    1980-01-01

    The present work is devoted to the presentation of analytical method for the calculation of elastically and inelastically slowed down neutrons in an infinite non-absorbing medium. On the basis of the central limit theory (CLT) and the integral transform technique the slowing down equation including inelastic scattering, in terms of the Green function of elastic scattering, is solved. The Green function is decomposed according to the number of collisions. Placzec discontinuity associated with elastic scattering in addition to two discontinuities due to inelastic scattering are investigated. Numerical calculations for Fe 56 show that the elastic discontinuity produces about 41.8% change in the collision density whilst the ratio of the inelastic collision density discontinuity at qsub(o)sup(+) to the Placzec discontinuity at usub(o) + 1n 1/oc gives 55.7 percent change. (author)

  8. Special discontinuities in nonlinearly elastic media

    Science.gov (United States)

    Chugainova, A. P.

    2017-06-01

    Solutions of a nonlinear hyperbolic system of equations describing weakly nonlinear quasitransverse waves in a weakly anisotropic elastic medium are studied. The influence of small-scale processes of dissipation and dispersion is investigated. The small-scale processes determine the structure of discontinuities (shocks) and a set of discontinuities with a stationary structure. Among the discontinuities with a stationary structure, there are special ones that, in addition to relations following from conservation laws, satisfy additional relations required for the existence of their structure. In the phase plane, the structure of such discontinuities is represented by an integral curve joining two saddles. Special discontinuities lead to nonunique self-similar solutions of the Riemann problem. Asymptotics of non-self-similar problems for equations with dissipation and dispersion are found numerically. These asymptotics correspond to self-similar solutions of the problems.

  9. Historical transformation and epistemological discontinuity

    Directory of Open Access Journals (Sweden)

    Močnik Rastko

    2013-01-01

    Full Text Available Starting from recent formulas of EU bureaucracy for subordinating scientific and educational apparatuses to the needs of the capital and to the requests of its political representatives, the article analyses the interconnection between the historical transformation of the ideological state apparatuses (universities, higher education institutions, research institutes etc. and the epistemological discontinuity provoked by the triumph of technosciences. The hypothesis to be tested is the following: While the crisis of West European-North American capitalism requires an ever tighter submission of ideological state apparatuses, and especially of scientific and academic apparatuses to the needs of the capital, theoretical practices in the humanities and social sciences have come to the point where they entered into an open conflict with the domination of the capital and have, as a consequence, started to subvert their own institutional supports in the ideological apparatuses of the capitalist state. For this purpose, the article reconsiders social sciences as a compromise formation and, eventually, reassesses the historical materialism as a non-Cartesian modern science.

  10. Polylinear regression analysis in radiochemistry

    International Nuclear Information System (INIS)

    Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.

    1995-01-01

    A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis

  11. Linear Regression Models for Estimating True Subsurface ...

    Indian Academy of Sciences (India)

    47

    The objective is to minimize the processing time and computer memory required. 10 to carry out inversion .... to the mainland by two long bridges. .... term. In this approach, the model converges when the squared sum of the differences. 143.

  12. Changes in renal function after discontinuation of vitamin D analogues in advanced chronic kidney disease.

    Science.gov (United States)

    Caravaca, Francisco; Caravaca-Fontán, Fernando; Azevedo, Lilia; Luna, Enrique

    In routine clinical practice, the prescription of vitamin D analogues (VDA) in patients with chronic kidney disease (CKD) is often associated with a decline of the estimated renal function. The reason for this is not fully understood. To analyse the effects of VDA discontinuation in advanced CKD and to determine the factors associated with changes in renal function. Retrospective cohort study of adult patients with advanced CKD. The case subgroup was treated with VDA and this medication was discontinued at baseline (the first visit). The control subgroup was not treated with VDA and they were selected according to comparability principles for CKD progression by propensity score matching. The primary outcome measure was a change to both the estimated glomerular filtration rate (MDRD-GFR) and the measured glomerular filtration rate (mGFR by combined creatinine and urea clearances). Baseline parameters related to mineral metabolism and creatinine generation were analysed as potential determinants of renal function changes. The study sample consisted of 67 cases and 67 controls. Renal function improved in 67% of cases and worsened in 72% of controls (p<0.0001). Changes in MDRD-GFR for the case subgroup and the control subgroup were +0.455±0.997 vs. -0.436±1.103ml/min/1.73 m 2 /month (p<0.0001), respectively. Total creatinine excretion was slightly higher in cases than in controls but the difference was not significant. According to multivariate logistic and linear regression analyses, baseline total serum calcium was one of the best determinants of both renal function recovery (Odds ratio=3.49; p=0.001), and of the extent of renal function recovery (beta=0.276; p=0.001). Discontinuation of VDA treatment in CKD patients is associated with significant recovery of estimated renal function. The extent of these changes is mainly associated with baseline total serum calcium. Copyright © 2017 Sociedad Española de Nefrología. Published by Elsevier España, S.L.U. All

  13. Regression to Causality : Regression-style presentation influences causal attribution

    DEFF Research Database (Denmark)

    Bordacconi, Mats Joe; Larsen, Martin Vinæs

    2014-01-01

    of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

  14. Quantile Regression With Measurement Error

    KAUST Repository

    Wei, Ying

    2009-08-27

    Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.

  15. Estimação de parâmetros genéticos para produção de leite de vacas da raça Holandesa via regressão aleatória Estimation of genetic parameters for Holstein cows milk production by random regression

    Directory of Open Access Journals (Sweden)

    C.K.P. Dorneles

    2009-04-01

    Full Text Available Foram utilizados 21.702 registros de produção de leite no dia do controle de 2.429 vacas primíparas da raça Holandesa, filhas de 233 touros, coletados em 33 rebanhos do Estado do Rio Grande do Sul, para estimar parâmetros genéticos para produção de leite no dia do controle. O modelo de regressão aleatória ajustado aos controles leiteiros entre o sexto e o 305º dia de lactação incluiu o efeito de rebanho-ano-mês do controle, idade da vaca no parto e os parâmetros do polinômio de Legendre de ordem quatro, para modelar a curva média da produção de leite da população e parâmetros do mesmo polinômio, para modelar os efeitos aleatórios genético-aditivo e de ambiente permanente. As variâncias genéticas e de ambiente permanente para produção de leite no dia do controle variaram, respectivamente, de 2,38 a 3,14 e de 7,55 a 10,35. As estimativas de herdabilidade aumentaram gradativamente do início (0,14 para o final do período de lactação (0,20, indicando ser uma característica de moderada herdabilidade. As correlações genéticas entre as produções de leite de diferentes estágios leiteiros variaram de 0,33 a 0,99 e foram maiores entre os controles adjacentes. As correlações de ambiente permanente seguiram a mesma tendência das correlações genéticas. O modelo de regressão aleatória com polinômio de Legendre de ordem quatro pode ser considerado como uma boa ferramenta para estimação de parâmetros genéticos para a produção de leite ao longo da lactação.A total of 21,702 records of milk production from 2,429 first-lactation Holstein cows, sired by 233 bulls, collected in 33 herds in the State of Rio Grande do Sul from 1991 to 2003, were used to estimate genetic parameters for that characteristic. The random regression model adjusted to test day from the 6th and the 305th lactation day included the effect of herd-year-month of the test day, the age of the cow at parturition, and the order fourth Legendre

  16. Regression analysis with categorized regression calibrated exposure: some interesting findings

    Directory of Open Access Journals (Sweden)

    Hjartåker Anette

    2006-07-01

    Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

  17. Multivariate and semiparametric kernel regression

    OpenAIRE

    Härdle, Wolfgang; Müller, Marlene

    1997-01-01

    The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...

  18. Early Discontinuation of Metformin in Individuals Treated with Inhibitors of Transporters of Metformin

    DEFF Research Database (Denmark)

    Stage, Tore Bjerregaard; Lee, Moa P; Hallas, Jesper

    2016-01-01

    The aim of this study was to examine the risk of early discontinuation of metformin as a proxy for intolerance, associated with use of drugs known to inhibit transporters involved in metformin distribution. We analysed all incident users of metformin in Denmark between 2000 and 2012 (n = 132......,221) and in a cohort of US patients (n = 296,903). Risk of early discontinuation of metformin was assessed using adjusted logistic regression for 28 drugs putatively inhibiting metformin transporters and four negative controls. Increased odds ratio of early discontinuation of metformin was only associated with codeine...... drugs were associated with a decreased risk. These findings indicate that codeine use may be associated with risk of early discontinuation of metformin and could be used as a basis for further investigation....

  19. Parâmetros genéticos para a produção de leite de controles individuais de vacas da raça Gir estimados com modelos de repetibilidade e regressão aleatória Estimation of genetic parameters for test day milk records of first lactation Gyr cows using repeatability and random regression animal models

    Directory of Open Access Journals (Sweden)

    Claudio Napolis Costa

    2005-10-01

    número de estimativas negativas entre as PLC do início e fim da lactação do que a FAS. Exceto para a FAS, observou-se redução das estimativas de correlação genética próximas à unidade entre as PLC adjacentes para valores negativos entre as PLC no início e no fim da lactação. Entre os polinômios de Legendre, o de quinta ordem apresentou um melhor o ajuste das PLC. Os resultados indicam o potencial de uso de regressão aleatória, com os modelos LP5 e a FAS apresentando-se como os mais adequados para a modelagem das variâncias genética e de efeito permanente das PLC da raça Gir.Data comprising 8,183 test day records of 1,273 first lactations of Gyr cows from herds supervised by ABCZ were used to estimate variance components and genetic parameters for milk yield using repeatability and random regression animal models by REML. Genetic modelling of logarithmic (FAS, exponential (FW curves was compared to orthogonal Legendre polynomials (LP of order 3 to 5. Residual variance was assumed to be constant in all (ME=1 or some periods of lactation (ME=4. Lactation milk yield in 305-d was also adjusted by an animal model. Genetic variance, heritability and repeatability for test day milk yields estimated by a repeatability animal model were 1.74 kg2, 0.27, and 0.76, respectively. Genetic variance and heritability estimates for lactation milk yield were respectively 121,094.6 and 0.22. Heritability estimates from FAS and FW, respectively, decreased from 0,59 and 0.74 at the beginning of lactation to 0.20 at the end of the period. Except for a fifth-order LP with ME=1, heritability estimates decreased from around 0,70 at early lactation to 0,30 at the end of lactation. Residual variance estimates were slightly smaller for logarithimic than for exponential curves both for homogeneous and heterogeneous variance assumptions. Estimates of residual variance in all stages of lactation decreased as the order of LP increased and depended on the assumption about ME

  20. Thermal classification of lithospheric discontinuities beneath USArray

    Science.gov (United States)

    Hansen, Steven M.; Dueker, Ken; Schmandt, Brandon

    2015-12-01

    Broadband seismic data from the United States were processed into Ps and Sp receiver function image volumes for the purpose of constraining negative velocity gradients (NVG) at depths between the Moho and 200 km. Moho depth picks from the two independent datasets are in good agreement, however, large discrepancies in NVG picks occur and are attributed to free-surface multiples which obscure deep NVG arrivals in the Ps data. From the Sp data, shallow NVG are found west of the Rockies and in the central US while deep and sporadic NVG are observed beneath the Great Plains and northern Rockies. To aid the interpretation of the observed NVG arrivals, the mantle thermal field is estimated by mapping surface wave tomography velocities to temperature assuming an anelastic olivine model. The distribution of temperature versus NVG depth is bi-modal and displays two distinct thermal populations that are interpreted to represent both the lithosphere-asthenosphere boundary (LAB) and mid-lithosphere discontinuities (MLD). LAB arrivals occur in the western US at 60-85 km and 1200-1400 °C depth suggesting that they manifest partial melt near the base of the thermal plate. MLD arrivals primarily occur at 70-110 km depth and 700-900 °C and we hypothesize that these arrivals are caused by a low-velocity metasomatic layer containing phlogopite resulting from magma crystallization products that accumulate within long-lived thick lithosphere.

  1. Forecasting exchange rates: a robust regression approach

    OpenAIRE

    Preminger, Arie; Franck, Raphael

    2005-01-01

    The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...

  2. Ultrasonic assessment of shrinkage type discontinuities

    International Nuclear Information System (INIS)

    Hubber, John

    2010-01-01

    This investigation into ultrasonic internal discontinuities is intended to demonstrate typical examples of internal 'shrinkage' type discontinuities and its connection with the casting suitability, integrity and reliability in service. This type of discontinuity can be misinterpreted by ultrasonic technicians and can lead to the rejection of castings unnecessarily, due to the mis-characterization of fine shrinkage - discrete porosity. The samples for this investigation were taken from thirty ton heavy section ductile iron mill flange castings, manufactured by Graham Campbell Ferrum International. The sampled area was of discontinuities that were recorded for sizing on an area due to loss of back wall echo, but had acceptable reflectivity. A comparative sample was taken adjacent to the area of discrete porosity. The discontinuities found by this investigation are of a 'spongy' type, gaseous in appearance and are surrounded by acoustically sound material. All discontinuities discussed in this paper are centrally located in the through thickness of the casting. The porous nature of this type of discontinuity consisting of approximately 80-90% metal has its own residual strength, as indicated by the proof stress results which reveal a residual strength of up to 50-60% of that of the unaffected area of the casting. The affected areas are elliptical in shape and vary in density and through thickness throughout.

  3. Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

    Science.gov (United States)

    Bulcock, J. W.

    The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

  4. Modelos de regressão com platô na estimativa do tamanho de parcelas em experimento de conservação in vitro de maracujazeiro Using of regression plateau models in estimation of plot sizes for experiments with passion fruit

    Directory of Open Access Journals (Sweden)

    Ana Patricia Bastos Peixoto

    2011-11-01

    Full Text Available A determinação do tamanho de parcela é uma prática pertinente ao planejamento experimental e sua caracterização otimizada, em conjunto com o controle do material experimental, permite a obtenção de resultados com maior precisão e qualidade. Neste trabalho, determinou-se o tamanho de parcelas para experimentos de conservação in vitro de maracujazeiro, em dez ensaios de uniformidade com a espécie Passiflora Giberti N. E. Brown, utilizando-se o modelo de regressão linear segmentado com platô e o modelo de regressão quadrática segmentado com platô, que utilizam a técnica de resposta com platô a modelos que possuam mínimo. Os ensaios de uniformidade foram oriundos de experimento conduzido no delineamento inteiramente casualizado, com 20 unidades básicas (ub, e os tratamentos dispostos em esquema fatorial com três concentrações de sacarose, três concentrações de sorbitol e uma testemunha. A coleta dos dados foi realizada aos 60 dias após a incubação, medindo-se o comprimento das brotações. Os tamanhos de parcelas variaram com o método utilizado, encontrando-se parcelas formadas por seis explantes pelo modelo da regressão linear segmentado com platô e de dez explantes pelo modelo de regressão quadrática segmentado com platô.The determination of the plot size is a practical question to the experimental design, and its characterization in an optimized way allows obtaining larger precision and quality results. This research aimed to determine the plot size in experiments in vitro that seek the passion fruit plant conservation in ten uniformity assays with the species Passiflora Giberti N. E. Brown. The tests of uniformity came from an experiment conducted in a completely randomized design with treatments in a factorial design with three concentrations of sucrose, three concentrations of sorbitol and a control. Each treatment was considered as a uniformity assay, with 20 basic units. The evaluations of the experiments

  5. Survival analysis II: Cox regression

    NARCIS (Netherlands)

    Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.

    2011-01-01

    In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the

  6. Kernel regression with functional response

    OpenAIRE

    Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe

    2011-01-01

    We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.

  7. Regression with Sparse Approximations of Data

    DEFF Research Database (Denmark)

    Noorzad, Pardis; Sturm, Bob L.

    2012-01-01

    We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

  8. Signal integrity analysis on discontinuous microstrip line

    International Nuclear Information System (INIS)

    Qiao, Qingyang; Dai, Yawen; Chen, Zipeng

    2013-01-01

    In high speed PCB design, microstirp lines were used to control the impedance, however, the discontinuous microstrip line can cause signal integrity problems. In this paper, we use the transmission line theory to study the characteristics of microstrip lines. Research results indicate that the discontinuity such as truncation, gap and size change result in the problems such as radiation, reflection, delay and ground bounce. We change the discontinuities to distributed parameter circuits, analysed the steady-state response and transient response and the phase delay. The transient response cause radiation and voltage jump.

  9. Trapped particles at a magnetic discontinuity

    Science.gov (United States)

    Stern, D. P.

    1972-01-01

    At a tangential discontinuity between two constant magnetic fields a layer of trapped particles can exist, this work examines the conditions under which the current carried by such particles tends to maintain the discontinuity. Three cases are examined. If the discontinuity separates aligned vacuum fields, the only requirement is that they be antiparallel. With arbitrary relative orientations, the field must have equal intensities on both sides. Finally, with a guiding center plasma on both sides, the condition reduces to a relation which is also derivable from hydromagnetic theory. Arguments are presented for the occurrence of such trapped modes in the magnetopause and for the non-existence of specular particle reflection.

  10. Identification of a Discontinuous Parameter in Stochastic Parabolic Systems

    International Nuclear Information System (INIS)

    Aihara, S. I.

    1998-01-01

    The purpose of this paper is to study the identification problem for a spatially varying discontinuous parameter in stochastic diffusion equations. The consistency property of the maximum likelihood estimate (M.L.E.) and a generating algorithm for M.L.E. have been explored under the condition that the unknown parameter is in a sufficiently regular space with respect to spatial variables. In order to prove the consistency property of the M.L.E. for a discontinuous diffusion coefficient, we use the method of sieves, i.e., first the admissible class of unknown parameters is projected into a finite-dimensional space and next the convergence of the derived finite-dimensional M.L.E. to the infinite-dimensional M.L.E. is justified under some conditions. An iterative algorithm for generating the M.L.E. is also proposed with two numerical examples

  11. Finite element and discontinuous Galerkin methods for transient wave equations

    CERN Document Server

    Cohen, Gary

    2017-01-01

    This monograph presents numerical methods for solving transient wave equations (i.e. in time domain). More precisely, it provides an overview of continuous and discontinuous finite element methods for these equations, including their implementation in physical models, an extensive description of 2D and 3D elements with different shapes, such as prisms or pyramids, an analysis of the accuracy of the methods and the study of the Maxwell’s system and the important problem of its spurious free approximations. After recalling the classical models, i.e. acoustics, linear elastodynamics and electromagnetism and their variational formulations, the authors present a wide variety of finite elements of different shapes useful for the numerical resolution of wave equations. Then, they focus on the construction of efficient continuous and discontinuous Galerkin methods and study their accuracy by plane wave techniques and a priori error estimates. A chapter is devoted to the Maxwell’s system and the important problem ...

  12. Discontinuance of ADHD Treatment in Adolescents

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2009-04-01

    Full Text Available Prevalence of ADHD drug discontinuance in adolescents and young adults was studied in the UK by using the General Practice Database for patients aged 15-21 years from 1999 to 2006.

  13. Discontinuous Galerkin for the Radiative Transport Equation

    KAUST Repository

    Guermond, Jean-Luc; Kanschat, Guido; Ragusa, Jean C.

    2013-01-01

    This note presents some recent results regarding the approximation of the linear radiative transfer equation using discontinuous Galerkin methods. The locking effect occurring in the diffusion limit with the upwind numerical flux is investigated and a correction technique is proposed.

  14. Neural networks with discontinuous/impact activations

    CERN Document Server

    Akhmet, Marat

    2014-01-01

    This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

  15. Discontinuous Galerkin for the Radiative Transport Equation

    KAUST Repository

    Guermond, Jean-Luc

    2013-10-11

    This note presents some recent results regarding the approximation of the linear radiative transfer equation using discontinuous Galerkin methods. The locking effect occurring in the diffusion limit with the upwind numerical flux is investigated and a correction technique is proposed.

  16. Factors Influencing Contraceptive Choice and Discontinuation ...

    African Journals Online (AJOL)

    Erah

    2010-03-30

    women indicated that their HIV status dictated contraceptive decisions, particularly with ... Women reported method discontinuation because of side effects, having met desired parity, ...... Washington, D.C., 2009. ... Accessed March 30, 2010.

  17. Area Regge calculus and discontinuous metrics

    International Nuclear Information System (INIS)

    Wainwright, Chris; Williams, Ruth M

    2004-01-01

    Taking the triangle areas as independent variables in the theory of Regge calculus can lead to ambiguities in the edge lengths, which can be interpreted as discontinuities in the metric. We construct solutions to area Regge calculus using a triangulated lattice and find that on a spacelike or timelike hypersurface no such discontinuity can arise. On a null hypersurface however, we can have such a situation and the resulting metric can be interpreted as a so-called refractive wave

  18. Species Composition at the Sub-Meter Level in Discontinuous Permafrost in Subarctic Sweden

    Science.gov (United States)

    Anderson, S. M.; Palace, M. W.; Layne, M.; Varner, R. K.; Crill, P. M.

    2013-12-01

    Northern latitudes are experiencing rapid warming. Wetlands underlain by permafrost are particularly vulnerable to warming which results in changes in vegetative cover. Specific species have been associated with greenhouse gas emissions therefore knowledge of species compositional shift allows for the systematic change and quantification of emissions and changes in such emissions. Species composition varies on the sub-meter scale based on topography and other microsite environmental parameters. This complexity and the need to scale vegetation to the landscape level proves vital in our estimation of carbon dioxide (CO2) and methane (CH4) emissions and dynamics. Stordalen Mire (68°21'N, 18°49'E) in Abisko and is located at the edge of discontinuous permafrost zone. This provides a unique opportunity to analyze multiple vegetation communities in a close proximity. To do this, we randomly selected 25 1x1 meter plots that were representative of five major cover types: Semi-wet, wet, hummock, tall graminoid, and tall shrub. We used a quadrat with 64 sub plots and measured areal percent cover for 24 species. We collected ground based remote sensing (RS) at each plot to determine species composition using an ADC-lite (near infrared, red, green) and GoPro (red, blue, green). We normalized each image based on a Teflon white chip placed in each image. Textural analysis was conducted on each image for entropy, angular second momentum, and lacunarity. A logistic regression was developed to examine vegetation cover types and remote sensing parameters. We used a multiple linear regression using forwards stepwise variable selection. We found statistical difference in species composition and diversity indices between vegetation cover types. In addition, we were able to build regression model to significantly estimate vegetation cover type as well as percent cover for specific key vegetative species. This ground-based remote sensing allows for quick quantification of vegetation

  19. Regression analysis by example

    CERN Document Server

    Chatterjee, Samprit

    2012-01-01

    Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

  20. Logistic regression for dichotomized counts.

    Science.gov (United States)

    Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

    2016-12-01

    Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

  1. Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables Regressão logística para estimativa do bem-estar de matrizes pesadas em função de variáveis comportamentais e ambientais

    Directory of Open Access Journals (Sweden)

    Danilo F Pereira

    2011-02-01

    Full Text Available The increasing demand of consumer markets for the welfare of birds in poultry house has motivated many scientific researches to monitor and classify the welfare according to the production environment. Given the complexity between the birds and the environment of the aviary, the correct interpretation of the conduct becomes an important way to estimate the welfare of these birds. This study obtained multiple logistic regression models with capacity of estimating the welfare of broiler breeders in relation to the environment of the aviaries and behaviors expressed by the birds. In the experiment, were observed several behaviors expressed by breeders housed in a climatic chamber under controlled temperatures and three different ammonia concentrations from the air monitored daily. From the analysis of the data it was obtained two logistic regression models, of which the first model uses a value of ammonia concentration measured by unit and the second model uses a binary value to classify the ammonia concentration that is assigned by a person through his olfactory perception. The analysis showed that both models classified the broiler breeder's welfare successfully.As crescentes demandas e exigências dos mercados consumidores pelo bem-estar das aves nos aviários têm motivado diversas pesquisas científicas a monitorar e a classificar o bem-estar em função do ambiente de criação. Diante da complexidade com que as aves interagem com o ambiente do aviário, a correta interpretação dos comportamentos torna-se uma importante maneira para estimar o bem-estar dessas aves. Este trabalho criou modelos de regressão logística múltipla capazes de estimar o bem-estar de matrizes pesadas em função do ambiente do aviário e dos comportamentos expressos pelas aves. No experimento, foram observados diversos comportamentos expressos por matrizes pesadas alojadas em câmara climática sob três temperaturas controladas e diferentes concentrações de am

  2. Management applications of discontinuity theory | Science ...

    Science.gov (United States)

    1.Human impacts on the environment are multifaceted and can occur across distinct spatiotemporal scales. Ecological responses to environmental change are therefore difficult to predict, and entail large degrees of uncertainty. Such uncertainty requires robust tools for management to sustain ecosystem goods and services and maintain resilient ecosystems. 2.We propose an approach based on discontinuity theory that accounts for patterns and processes at distinct spatial and temporal scales, an inherent property of ecological systems. Discontinuity theory has not been applied in natural resource management and could therefore improve ecosystem management because it explicitly accounts for ecological complexity. 3.Synthesis and applications. We highlight the application of discontinuity approaches for meeting management goals. Specifically, discontinuity approaches have significant potential to measure and thus understand the resilience of ecosystems, to objectively identify critical scales of space and time in ecological systems at which human impact might be most severe, to provide warning indicators of regime change, to help predict and understand biological invasions and extinctions and to focus monitoring efforts. Discontinuity theory can complement current approaches, providing a broader paradigm for ecological management and conservation This manuscript provides insight on using discontinuity approaches to aid in managing complex ecological systems. In part

  3. The Application of Classification and Regression Trees for the Triage of Women for Referral to Colposcopy and the Estimation of Risk for Cervical Intraepithelial Neoplasia: A Study Based on 1625 Cases with Incomplete Data from Molecular Tests

    Directory of Open Access Journals (Sweden)

    Abraham Pouliakis

    2015-01-01

    Full Text Available Objective. Nowadays numerous ancillary techniques detecting HPV DNA and mRNA compete with cytology; however no perfect test exists; in this study we evaluated classification and regression trees (CARTs for the production of triage rules and estimate the risk for cervical intraepithelial neoplasia (CIN in cases with ASCUS+ in cytology. Study Design. We used 1625 cases. In contrast to other approaches we used missing data to increase the data volume, obtain more accurate results, and simulate real conditions in the everyday practice of gynecologic clinics and laboratories. The proposed CART was based on the cytological result, HPV DNA typing, HPV mRNA detection based on NASBA and flow cytometry, p16 immunocytochemical expression, and finally age and parous status. Results. Algorithms useful for the triage of women were produced; gynecologists could apply these in conjunction with available examination results and conclude to an estimation of the risk for a woman to harbor CIN expressed as a probability. Conclusions. The most important test was the cytological examination; however the CART handled cases with inadequate cytological outcome and increased the diagnostic accuracy by exploiting the results of ancillary techniques even if there were inadequate missing data. The CART performance was better than any other single test involved in this study.

  4. The Application of Classification and Regression Trees for the Triage of Women for Referral to Colposcopy and the Estimation of Risk for Cervical Intraepithelial Neoplasia: A Study Based on 1625 Cases with Incomplete Data from Molecular Tests.

    Science.gov (United States)

    Pouliakis, Abraham; Karakitsou, Efrossyni; Chrelias, Charalampos; Pappas, Asimakis; Panayiotides, Ioannis; Valasoulis, George; Kyrgiou, Maria; Paraskevaidis, Evangelos; Karakitsos, Petros

    2015-01-01

    Nowadays numerous ancillary techniques detecting HPV DNA and mRNA compete with cytology; however no perfect test exists; in this study we evaluated classification and regression trees (CARTs) for the production of triage rules and estimate the risk for cervical intraepithelial neoplasia (CIN) in cases with ASCUS+ in cytology. We used 1625 cases. In contrast to other approaches we used missing data to increase the data volume, obtain more accurate results, and simulate real conditions in the everyday practice of gynecologic clinics and laboratories. The proposed CART was based on the cytological result, HPV DNA typing, HPV mRNA detection based on NASBA and flow cytometry, p16 immunocytochemical expression, and finally age and parous status. Algorithms useful for the triage of women were produced; gynecologists could apply these in conjunction with available examination results and conclude to an estimation of the risk for a woman to harbor CIN expressed as a probability. The most important test was the cytological examination; however the CART handled cases with inadequate cytological outcome and increased the diagnostic accuracy by exploiting the results of ancillary techniques even if there were inadequate missing data. The CART performance was better than any other single test involved in this study.

  5. Fitness in animals correlates with proximity to discontinuities in body mass distributions.

    Science.gov (United States)

    Angeler, David G.; Allen, Craig R.; Vila-Gispert, Anna; Almeida, David

    2014-01-01

    Discontinuous structure in landscapes may cause discontinuous, aggregated species body-mass patterns, reflecting the scales of structure available to animal communities within a landscape. Empirical analyses have shown that the location of species within body mass aggregations, which reflect this scale-specific organization, is non-random with regard to several ecological phenomena, including species extinctions. The propensity of declining species to have body masses proximate to discontinuities suggests that transition zones between scaling regimes ultimately decreases the ecological fitness for some species. We test this proposition using vulnerable and unthreatened fish species in Mediterranean streams with differing levels of human impact. We show that the proximity to discontinuities in body mass aggregations (“distance-to-edge”) of more vs. less fit individuals within vulnerable and unthreatened populations differs. Specifically, regression analysis between the scaled mass index, a proxy of animal fitness, and distance-to-edge reveals negative and positive relationships for vulnerable and unthreatened species, respectively. That is, fitness is higher close to discontinuities in vulnerable populations and toward the center of body mass aggregation groups in unthreatened populations. Our results demonstrate the suitability of the discontinuity framework for scrutinizing non-random patterns of environmental impact in populations. Further exploration of the usefulness of this method across other ecosystems and organism groups is warranted.

  6. Contraceptive discontinuation and switching among Ghanaian women: evidence from the Ghana Demographic and Health Survey, 2008.

    Science.gov (United States)

    Modey, Emefa J; Aryeetey, Richmond; Adanu, Richard

    2014-03-01

    This study identifies factors associated with contraceptive discontinuation and switching among Ghanaian women of reproductive age, using data from 1,378 female respondents of the 2008 Ghana Demographic and Health Survey. Logistic regression models were used to determine relationships between key socio-demographic factors and user status. Discontinued use occurred among 56% of ever users and switching among 55% of current users. The IUD was most abandoned (70%) and its use was associated with almost twice the odds of discontinuation (OR = 1.97; 95% CI (1.04, 3.75)). Having a history of terminated pregnancy significantly predicted both discontinuation (OR = 1.36; 95% CI (1.03, 1.79) and switching (OR = 1.78; 95% CI (1.16, 2.73)) and intention to limit births significantly predicted lower discontinuation (OR = 0.71; 95% CI (0.52, 0.96)). Counseling services emphasizing contraceptive options and reinforcing switching are critically needed to reduce unwanted pregnancies that may result from poor method use and discontinuation especially among post-abortion clients and IUD users.

  7. Clinician support and psychosocial risk factors associated with breastfeeding discontinuation.

    Science.gov (United States)

    Taveras, Elsie M; Capra, Angela M; Braveman, Paula A; Jensvold, Nancy G; Escobar, Gabriel J; Lieu, Tracy A

    2003-07-01

    Breastfeeding rates fall short of goals set in Healthy People 2010 and other national recommendations. The current, national breastfeeding continuation rate of 29% at 6 months lags behind the Healthy People 2010 goal of 50%. The objective of this study was to evaluate associations between breastfeeding discontinuation at 2 and 12 weeks postpartum and clinician support, maternal physical and mental health status, workplace issues, and other factors amenable to intervention. A prospective cohort study was conducted of low-risk mothers and infants who were in a health maintenance organization and enrolled in a randomized, controlled trial of home visits. Mothers were interviewed in person at 1 to 2 days postpartum and by telephone at 2 and 12 weeks. Logistic regression modeling was performed to assess the independent effects of the predictors of interest, adjusting for sociodemographic and other confounding variables. Of the 1163 mother-newborn pairs in the cohort, 1007 (87%) initiated breastfeeding, 872 (75%) were breastfeeding at the 2-week interview, and 646 (55%) were breastfeeding at the 12-week interview. In the final multivariate models, breastfeeding discontinuation at 2 weeks was associated with lack of confidence in ability to breastfeed at the 1- to 2-day interview (odds ratio [OR]: 2.8; 95% confidence interval [CI]: 1.02-7.6), early breastfeeding problems (OR: 1.5; 95% CI: 1.1-1.97), Asian race/ethnicity (OR: 2.6; 95% CI: 1.1-5.7), and lower maternal education (OR: 1.5; 95% CI: 1.2-1.9). Mothers were much less likely to discontinue breastfeeding at 12 weeks postpartum if they reported (during the 12-week interview) having received encouragement from their clinician to breastfeed (OR: 0.6; 95% CI: 0.4-0.8). Breastfeeding discontinuation at 12 weeks was also associated with demographic factors and maternal depressive symptoms (OR: 1.18; 95% CI: 1.01-1.37) and returning to work or school by 12 weeks postpartum (OR: 2.4; 95% CI: 1.8-3.3). Our results indicate

  8. Comparison of Regression and Neural Networks Models to Estimate Solar Radiation Comparación de Regresión y Modelos de Redes Neuronales para Estimar la Radiación Solar

    Directory of Open Access Journals (Sweden)

    Mónica Bocco

    2010-09-01

    Full Text Available The incident solar radiation on soil is an important variable used in agricultural applications; it is also relevant in hydrology, meteorology and soil physics, among others. To estimate this variable, empirical models have been developed using several parameters and, recently, prognostic and prediction models based on artificial intelligence techniques such as neural networks. The aim of this work was to develop linear models and neural networks, multilayer perceptron, to estimate daily global solar radiation and compare their efficiency in its application to a region of the Province of Salta, Argentina. Relative sunshine duration, maximum and minimum temperature, rainfall, binary rainfall and extraterrestrial solar radiation data for the period 1996-2002, were used. All data were supplied by Experimental Station Salta, Instituto Nacional de Tecnología Agropecuaria (INTA, Argentina. For both, neural networks models and linear regressions, three alternative combinations of meteorological parameters were considered. Good results with both prediction methods were obtained, with root mean square error (RMSE values between 1.99 and 1.66 MJ m-2 d-1 for linear regressions and neural networks, and coefficients of correlation (r² between 0.88 and 0.92, respectively. Even though neural networks and linear regression models can be used to predict the daily global solar radiation appropriately, neural networks produced better estimates.La radiación solar incidente en el suelo es una variable importante usada en aplicaciones agronómicas, además es relevante en hidrología, meteorología y física del suelo, entre otros. Para estimarla se han desarrollado modelos empíricos que utilizan distintos parámetros meteorológicos y, recientemente, modelos de pronóstico y predicción basados en técnicas de inteligencia artificial tales como redes neuronales. El objetivo de este trabajo fue desarrollar modelos lineales y de redes neuronales, del tipo perceptr

  9. Geographically weighted regression as a generalized Wombling to detect barriers to gene flow.

    Science.gov (United States)

    Diniz-Filho, José Alexandre Felizola; Soares, Thannya Nascimento; de Campos Telles, Mariana Pires

    2016-08-01

    Barriers to gene flow play an important role in structuring populations, especially in human-modified landscapes, and several methods have been proposed to detect such barriers. However, most applications of these methods require a relative large number of individuals or populations distributed in space, connected by vertices from Delaunay or Gabriel networks. Here we show, using both simulated and empirical data, a new application of geographically weighted regression (GWR) to detect such barriers, modeling the genetic variation as a "local" linear function of geographic coordinates (latitude and longitude). In the GWR, standard regression statistics, such as R(2) and slopes, are estimated for each sampling unit and thus are mapped. Peaks in these local statistics are then expected close to the barriers if genetic discontinuities exist, capturing a higher rate of population differentiation among neighboring populations. Isolation-by-Distance simulations on a longitudinally warped lattice revealed that higher local slopes from GWR coincide with the barrier detected with Monmonier algorithm. Even with a relatively small effect of the barrier, the power of local GWR in detecting the east-west barriers was higher than 95 %. We also analyzed empirical data of genetic differentiation among tree populations of Dipteryx alata and Eugenia dysenterica Brazilian Cerrado. GWR was applied to the principal coordinate of the pairwise FST matrix based on microsatellite loci. In both simulated and empirical data, the GWR results were consistent with discontinuities detected by Monmonier algorithm, as well as with previous explanations for the spatial patterns of genetic differentiation for the two species. Our analyses reveal how this new application of GWR can viewed as a generalized Wombling in a continuous space and be a useful approach to detect barriers and discontinuities to gene flow.

  10. Sleep discontinuity and impaired sleep continuity affect transition to and from obesity over time: results from the Alameda county study.

    Science.gov (United States)

    Nordin, Maria; Kaplan, Robert M

    2010-03-01

    To investigate the impact of development in sleep continuity on transition to and from obesity over time. The study used self-reported sleep and body mass index (BMI) measures from the 1965, 1974, 1983, and 1994 waves of the longitudinal Alameda County Study. Sleep continuity was assessed by a question on whether the participants had any troubles falling or staying asleep. Change in sleep and BMI were estimated from the sleep and BMI questions in 1965 and 1994 respectively. Multinomial regression analyses were used to examine the risk/chance for a transition to and from obesity (BMI >or=30 kg/m(2)) due to development in sleep continuity. After adjustment for confounding variables, consistent sleep discontinuity both increases the risk for a transition to obesity and reduces the chance of losing weight, whereas impaired sleep continuity lowers the chance for weight loss. Effects for obesity were non-significant for those with improved sleep continuity. Consistent sleep discontinuity and impaired sleep continuity increases the risk of transition to obesity or of remaining obese.

  11. Understanding logistic regression analysis

    OpenAIRE

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

  12. Discontinuation Decision in Assisted Reproductive Techniques

    Directory of Open Access Journals (Sweden)

    Ashraf Moini

    2009-01-01

    Full Text Available Background: In vitro fertilization (IVF and intra cytoplasmic sperm injection (ICSI are recognizedas established and increasingly successful forms of treatment for infertility, yet significant numbersof couples discontinue treatment without achieving a live birth. This study aims to identify majorfactors that influence the decision to discontinue IVF/ICSI treatments.Materials and Methods: We studied the data of 338 couples who discontinued their infertilitytreatments after three cycles; based on medical records and phone contact. The main measure wasthe reason for stopping their treatments.Results: Economical problems were cited by 212 couples (62.7%, as their mean income wassignificantly less than other couples (p<0.0001. Lack of success was reported as a reason by229 (67.8%, from whom 165 (72% also had economical problems. Achieving independent-ART pregnancy was the reason for discontinuation in 20 (5.9% couples. Psychological stress,depression and anxiety were reported as other cessation factors by 169 (50%, 148 (43.8% and 182(53.8% couples, respectively.Conclusion: This survey suggests that the most common reasons for assisted reproductivetechnique (ART discontinuation after three cycles are: prior unsuccessful cycles, economicaland psychological problems. Therefore, the substantial proportion of couples could benefit frompsychological intervention, increasing awareness of ART outcomes and health funding to copemore adequately with failed treatments.

  13. Discontinuous Galerkin Method for Hyperbolic Conservation Laws

    KAUST Repository

    Mousikou, Ioanna

    2016-11-11

    Hyperbolic conservation laws form a special class of partial differential equations. They describe phenomena that involve conserved quantities and their solutions show discontinuities which reflect the formation of shock waves. We consider one-dimensional systems of hyperbolic conservation laws and produce approximations using finite difference, finite volume and finite element methods. Due to stability issues of classical finite element methods for hyperbolic conservation laws, we study the discontinuous Galerkin method, which was recently introduced. The method involves completely discontinuous basis functions across each element and it can be considered as a combination of finite volume and finite element methods. We illustrate the implementation of discontinuous Galerkin method using Legendre polynomials, in case of scalar equations and in case of quasi-linear systems, and we review important theoretical results about stability and convergence of the method. The applications of finite volume and discontinuous Galerkin methods to linear and non-linear scalar equations, as well as to the system of elastodynamics, are exhibited.

  14. Discontinuous Galerkin Method for Hyperbolic Conservation Laws

    KAUST Repository

    Mousikou, Ioanna

    2016-01-01

    Hyperbolic conservation laws form a special class of partial differential equations. They describe phenomena that involve conserved quantities and their solutions show discontinuities which reflect the formation of shock waves. We consider one-dimensional systems of hyperbolic conservation laws and produce approximations using finite difference, finite volume and finite element methods. Due to stability issues of classical finite element methods for hyperbolic conservation laws, we study the discontinuous Galerkin method, which was recently introduced. The method involves completely discontinuous basis functions across each element and it can be considered as a combination of finite volume and finite element methods. We illustrate the implementation of discontinuous Galerkin method using Legendre polynomials, in case of scalar equations and in case of quasi-linear systems, and we review important theoretical results about stability and convergence of the method. The applications of finite volume and discontinuous Galerkin methods to linear and non-linear scalar equations, as well as to the system of elastodynamics, are exhibited.

  15. Prevalence and associated factors of contraceptive discontinuation and switching among Bangladeshi married women of reproductive age

    Directory of Open Access Journals (Sweden)

    Mahumud RA

    2015-01-01

    Full Text Available Rashidul Alam Mahumud,1 Md Golam Hossain,2 Abdur Razzaque Sarkar,1 Md Nurul Islam,2 Md Ripter Hossain,2 Aik Saw,3 Jahangir AM Khan1,4 1Health Economics and Financing Research Group, Center for Equity and Health Systems, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh; 2Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh; 3Department of Orthopaedic Surgery, National Orthopaedic Centre of Excellence for Research and Learning, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; 4Adjunct Faculty, Health Economics Unit, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden Introduction: Contraceptive discontinuation is a worldwide incident that may be connected with low incentive to avoid pregnancy. Contraceptive discontinuation highly contributes to unplanned pregnancy and unwanted births.Objectives: The objective of this study was to observe the prevalence of discontinuation and switching of contraceptive methods among Bangladeshi married women. In addition, the sociodemographic factors associated with contraceptive discontinuation and switching were assessed.Methods: Secondary cross-sectional data was used in this study. A total of 16,273 married Bangladeshi women of reproductive age (15–49 years were considered in the present study, from the Bangladesh Demographic and Health Survey, 2011. Logistic regression models were used to determine the relationships between key sociodemographic factors and user status.Results: The prevalence of discontinuation and switching of contraceptive method among women were 38.4% and 15.4%, respectively. The logistic regression model demonstrated that women in early reproductive years (25–29 years and 30–34 years significantly more often (odds ratio [OR] =0.84 and 0.71, respectively discontinued use of contraceptives. Significantly higher rates of discontinuation were pronounced among women who

  16. Introduction to regression graphics

    CERN Document Server

    Cook, R Dennis

    2009-01-01

    Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

  17. Alternative Methods of Regression

    CERN Document Server

    Birkes, David

    2011-01-01

    Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

  18. 27 CFR 555.128 - Discontinuance of business.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Discontinuance of business... Discontinuance of business. Where an explosive materials business or operations is discontinued and succeeded by... such facts and shall be delivered to the successor. Where discontinuance of the business or operations...

  19. 27 CFR 478.57 - Discontinuance of business.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Discontinuance of business... Licenses § 478.57 Discontinuance of business. (a) Where a firearm or ammunition business is either discontinued or succeeded by a new owner, the owner of the business discontinued or succeeded shall within 30...

  20. Financially troubled El Paso discontinues more nonutility operations

    International Nuclear Information System (INIS)

    Anon.

    1990-01-01

    As part of a plan to phase out its nonutility businesses, El Paso Electric Company will discontinue its remaining PascoTex Corporation operations, consisting of the manufacture of specialty steel products, and company management released revised estimates of losses to be incurred during the disposal period. Recently El Paso announced it would also bow out of most of its nonutility real estate operations. Increased operating expenses, principally at El Paso's Palo Verde station nuclear facility, have also impacted the bottom line. All three units at Palo Verde were out of service at the time of this writing. The good news was that energy sales have increased

  1. Mammographic density changes following discontinuation of tamoxifen in premenopausal women with oestrogen receptor-positive breast cancer.

    Science.gov (United States)

    Kim, Won Hwa; Cho, Nariya; Kim, Young-Seon; Yi, Ann

    2018-04-06

    To evaluate the changes in mammographic density after tamoxifen discontinuation in premenopausal women with oestrogen receptor-positive breast cancers and the underlying factors METHODS: A total of 213 consecutive premenopausal women with breast cancer who received tamoxifen treatment after curative surgery and underwent three mammograms (baseline, after tamoxifen treatment, after tamoxifen discontinuation) were included. Changes in mammographic density after tamoxifen discontinuation were assessed qualitatively (decrease, no change, or increase) by two readers and measured quantitatively by semi-automated software. The association between % density change and clinicopathological factors was evaluated using univariate and multivariate regression analyses. After tamoxifen discontinuation, a mammographic density increase was observed in 31.9% (68/213, reader 1) to 22.1% (47/213, reader 2) by qualitative assessment, with a mean density increase of 1.8% by quantitative assessment compared to density before tamoxifen discontinuation. In multivariate analysis, younger age (≤ 39 years) and greater % density decline after tamoxifen treatment (≥ 17.0%) were independent factors associated with density change after tamoxifen discontinuation (p density change with a mean density increase of 1.8%, which was associated with younger age and greater density change after tamoxifen treatment. • Increased mammographic density after tamoxifen discontinuation can occur in premenopausal women. • Mean density increase after tamoxifen discontinuation was 1.8%. • Density increase is associated with age and density decrease after tamoxifen.

  2. Continuous and discontinuous transitions to synchronization.

    Science.gov (United States)

    Wang, Chaoqing; Garnier, Nicolas B

    2016-11-01

    We describe how the transition to synchronization in a system of globally coupled Stuart-Landau oscillators changes from continuous to discontinuous when the nature of the coupling is moved from diffusive to reactive. We explain this drastic qualitative change as resulting from the co-existence of a particular synchronized macrostate together with the trivial incoherent macrostate, in a range of parameter values for which the latter is linearly stable. In contrast to the paradigmatic Kuramoto model, this particular state observed at the synchronization transition contains a finite, non-vanishing number of synchronized oscillators, which results in a discontinuous transition. We consider successively two situations where either a fully synchronized state or a partially synchronized state exists at the transition. Thermodynamic limit and finite size effects are briefly discussed, as well as connections with recently observed discontinuous transitions.

  3. Does Greater Autonomy Improve School Performance? Evidence from a Regression Discontinuity Analysis in Chicago

    Science.gov (United States)

    Steinberg, Matthew P.

    2014-01-01

    School districts throughout the United States are increasingly providing greater autonomy to local public (non-charter) school principals. In 2005-06, Chicago Public Schools initiated the Autonomous Management and Performance Schools program, granting academic, programmatic, and operational freedoms to select principals. This paper provides…

  4. School Outcomes in New South Wales and Queensland: A Regression Discontinuity Approach

    Science.gov (United States)

    Miller, Paul W.; Voon, Derby

    2014-01-01

    This paper examines the differences in school (NAPLAN) outcomes between New South Wales and Queensland. It shows that there are pronounced differences in Year 3 NAPLAN results between these states, though these dissipate when later class years are considered. The reasons for these state effects in school outcomes are explored using an empirical…

  5. Quantum field theory near surfaces of discontinuity

    International Nuclear Information System (INIS)

    Onishi, H.T.

    1981-01-01

    This work deals with the problem of a quantized scalar field propagating near a surface of discontinuity. The proper time formalism is employed to express the Green's function and stress tensor as proper time integrals of a transformation function. The transformation function is calculated by a WKB approximation which exhibits the essential singularities generated by the high frequency behavior of waves propagating near the surface. Two singularities are present, the usual direct singularity and an additional reflected singularity generated by the high frequency behavior of waves reflected by the discontinuity. The stress tensor is calculated by dimensional continuation. The results are employed to analyze energy generated by the surface

  6. News and views in discontinuous phase transitions

    Science.gov (United States)

    Nagler, Jan

    2014-03-01

    Recent progress in the theory of discontinuous percolation allow us to better understand the the sudden emergence of large-scale connectedness both in networked systems and on the lattice. We analytically study mechanisms for the amplification of critical fluctuations at the phase transition point, non-self-averaging and power law fluctuations. A single event analysis allow to establish criteria for discontinuous percolation transitions, even on the high-dimensional lattice. Some applications such as salad bowl percolation, and inverse fragmentation are discussed.

  7. MHD intermediate shock discontinuities: Pt. 1

    International Nuclear Information System (INIS)

    Kennel, C.F.; Blandford, R.D.; Coppi, P.

    1989-01-01

    Recent numerical investigations have focused attention once more on the role of intermediate shocks in MHD. Four types of intermediate shock are identified using a graphical representation of the MHD Rankine-Hugoniot conditions. This same representation can be used to exhibit the close relationship of intermediate shocks to switch-on shocks and rotational discontinuities. The conditions under which intermediate discontinuities can be found are elucidated. The variations in velocity, pressure, entropy and magnetic-field jumps with upstream parameters in intermediate shocks are exhibited graphically. The evolutionary arguments traditionally advanced against intermediate shocks may fail because the equations of classical MHD are not strictly hyperbolic. (author)

  8. Understanding logistic regression analysis.

    Science.gov (United States)

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

  9. Applied linear regression

    CERN Document Server

    Weisberg, Sanford

    2013-01-01

    Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

  10. Applied logistic regression

    CERN Document Server

    Hosmer, David W; Sturdivant, Rodney X

    2013-01-01

     A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

  11. Measuring persistence to hormonal therapy in patients with breast cancer: accounting for temporary treatment discontinuation.

    Science.gov (United States)

    Huiart, Laetitia; Ferdynus, Cyril; Dell'Aniello, Sophie; Bakiri, Naciba; Giorgi, Roch; Suissa, Samy

    2014-08-01

    Several studies have been conducted to estimate persistence to hormonal therapy among women with breast cancer (BC). Most studies focus on first treatment discontinuation. Patients, however, can have numerous periods of treatment discontinuation or treatment exposure. Our objective is to estimate persistence to tamoxifen in patients with BC while accounting for temporary treatment discontinuations and this by using multi-state (MS) models. A cohort of 10,806 women with BC having received at least one prescription of tamoxifen between 1998 and 2008 was constituted from the UK General Practice Research Database. We fitted a semi-Markov model with three states to estimate the probability of being off treatment over a 5-year period while accounting for temporary treatment discontinuations (transition between on treatment and off treatment) and competing risks (recurrence of BC or death). Non-persistence, as estimated from the MS model, ranged from 12.1% (95% confidence interval [95%CI]: 9.2-15.1) at 1 year to 14.9% (95%CI: 11.7-18.1) at 5 years. Estimations of non-persistence based on the Kaplan-Meier model were higher, i.e., 29.3% (95%CI: 28.1-30.6) at 5 years, as well as those obtained from a competing risk model, i.e., 24.0% (95%CI: 22.9-25.1). Most temporary discontinuations (94.7%) lasted less than 6 months. Temporary treatment discontinuations are frequent and should be accounted for when measuring adherence to treatment. MS models can provide a useful framework for this sort of analysis insofar as they help describe patients' complex behavior. This may help tailor interventions that improve persistence to hormonal therapy among women with BC. Copyright © 2014 John Wiley & Sons, Ltd.

  12. A Simulation Investigation of Principal Component Regression.

    Science.gov (United States)

    Allen, David E.

    Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…

  13. Mechanics of interfaces and evolving discontinuities

    NARCIS (Netherlands)

    De Borst, René; Remmers, Joris J C; Verhoosel, Clemens V.; Needleman, Alan; Zingoni, A.

    2013-01-01

    The two main approaches to the modelling of discontinuities are reviewed concisely, followed by a discussion of cohesive models for fracture. Emphasis is put on a novel approach to incorporate triaxiality into cohesive-zone models, and on the representation of cohesive crack models by phase-field

  14. Hybridized Multiscale Discontinuous Galerkin Methods for Multiphysics

    Science.gov (United States)

    2015-09-14

    local approximation spaces of the hybridizable discontinuous Galerkin methods with precomputed phases which are solutions of the eikonal equation in...geometrical optics. Second, we propose a systematic procedure for computing multiple solutions of the eikonal equation. Third, we utilize the eigenvalue

  15. Problems with Discontinuous Diffusion/Dispersion Coefficients

    Directory of Open Access Journals (Sweden)

    Stefano Ferraris

    2012-01-01

    accurate on smooth solutions and based on a special numerical treatment of the diffusion/dispersion coefficients that makes its application possible also when such coefficients are discontinuous. Numerical experiments confirm the convergence of the numerical approximation and show a good behavior on a set of benchmark problems in two space dimensions.

  16. Discontinuous precipitation in copper base alloys

    Indian Academy of Sciences (India)

    Discontinuous precipitation (DP) is associated with grain boundary migration in the wake of which alternate plates of the precipitate and the depleted matrix form. Some copper base alloys show DP while others do not. In this paper the misfit strain parameter, , has been calculated and predicted that if 100 > ± 0.1, DP is ...

  17. Trial discontinuation: lessons for future trial design?

    NARCIS (Netherlands)

    V.J.A.A. Nuij (Veerle); C.J. de Haar (Colin); C.J. van der Woude (Janneke)

    2012-01-01

    textabstractBackground: The current therapeutic arsenal still does not fulfill the therapeutic needs of inflammatory bowel disease patients. Although new drugs are constantly being developed, many will never reach the market. In this review we will search for reasons for discontinuing promising

  18. Controlling chaos in discontinuous dynamical systems

    International Nuclear Information System (INIS)

    Danca, Marius-F.

    2004-01-01

    In this paper we consider the possibility to implement the technique of changes in the system variables to control the chaos introduced by Gueemez and Matias for continuous dynamical systems to a class of discontinuous dynamical systems. The approach is realized via differential inclusions following the Filippov theory. Three practical examples are considered

  19. Forecasting Technological Discontinuities in the ICT Industry

    DEFF Research Database (Denmark)

    Hoisl, Karin; Stelzer, Tobias; Biala, Stefanie

    2015-01-01

    in the ICT industry. The conjoint approach allows for a simulation of the forecasting process and considers utility trade-offs. The results show that for both types of experts the perceived benefit of users most highly contributes to predicting technological discontinuities. Internal experts assign more...

  20. 77 FR 26185 - POSTNET Barcode Discontinuation

    Science.gov (United States)

    2012-05-03

    ... discontinue POSTNET barcodes for automation letter and flat price eligibility. There were six comments... on each piece, to facilitate processing by presort companies. We added language to specifically allow..., with or without prepayment of postage, for return to the address on the reply piece. If postage is...