Relationship Dissolution and Psychologically Aggressive Dating Relationships: Preliminary Findings From a College-Based Relationship Education Course.

Science.gov (United States)

Negash, Sesen; Cravens, Jaclyn D; Brown, Preston C; Fincham, Frank D

This study evaluated the impact of a relationship education program, delivered as part of a college course, among students (N = 152) who reported experiencing psychological aggression in their exclusive dating relationship. Preliminary results showed that compared to those in the control group, participants receiving relationship education were significantly more likely to end their romantic relationship, even after controlling for relationship satisfaction. Furthermore, when relationship termination occurred, those in the intervention group were significantly more likely to attribute the breakup to their participation in the class as compared to those in the control group. The tentative findings are an important preliminary step in assessing the benefits of relationship education in reducing the risk of psychological aggression among college students.

Brain responses to biological motion predict treatment outcome in young adults with autism receiving Virtual Reality Social Cognition Training: Preliminary findings.

Science.gov (United States)

Yang, Y J Daniel; Allen, Tandra; Abdullahi, Sebiha M; Pelphrey, Kevin A; Volkmar, Fred R; Chapman, Sandra B

2017-06-01

Autism Spectrum Disorder (ASD) is characterized by remarkable heterogeneity in social, communication, and behavioral deficits, creating a major barrier in identifying effective treatments for a given individual with ASD. To facilitate precision medicine in ASD, we utilized a well-validated biological motion neuroimaging task to identify pretreatment biomarkers that can accurately forecast the response to an evidence-based behavioral treatment, Virtual Reality-Social Cognition Training (VR-SCT). In a preliminary sample of 17 young adults with high-functioning ASD, we identified neural predictors of change in emotion recognition after VR-SCT. The predictors were characterized by the pretreatment brain activations to biological vs. scrambled motion in the neural circuits that support (a) language comprehension and interpretation of incongruent auditory emotions and prosody, and (b) processing socio-emotional experience and interpersonal affective information, as well as emotional regulation. The predictive value of the findings for individual adults with ASD was supported by regression-based multivariate pattern analyses with cross validation. To our knowledge, this is the first pilot study that shows neuroimaging-based predictive biomarkers for treatment effectiveness in adults with ASD. The findings have potentially far-reaching implications for developing more precise and effective treatments for ASD. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Landscape ecological impact of climatic change some preliminary findings of the LICC Project

International Nuclear Information System (INIS)

Boer, M.M.

1991-01-01

The main objectives of the LICC project are to address the potential effects of a future climatic change on (semi-) natural terrestrial ecosystems and landscapes in Europe; six case studies are covered: alpine regions, boreal and subartic regions, Mediterranean region, fluvial systems, wetlands and coastal dunes. Preliminary findings showed a serious lack in fundamental ecological knowledge. Assessment of potential effects involved changes in water and sediment fluxes, changes in the vegetation cover, species response, dispersal and migration in a fragmented landscape and modification of climate impacts by man

Narcissism and consumer behaviour: a review and preliminary findings

Science.gov (United States)

Cisek, Sylwia Z.; Sedikides, Constantine; Hart, Claire M.; Godwin, Hayward J.; Benson, Valerie; Liversedge, Simon P.

2014-01-01

We review the literature on the relation between narcissism and consumer behavior. Consumer behavior is sometimes guided by self-related motives (e.g., self-enhancement) rather than by rational economic considerations. Narcissism is a case in point. This personality trait reflects a self-centered, self-aggrandizing, dominant, and manipulative orientation. Narcissists are characterized by exhibitionism and vanity, and they see themselves as superior and entitled. To validate their grandiose self-image, narcissists purchase high-prestige products (i.e., luxurious, exclusive, flashy), show greater interest in the symbolic than utilitarian value of products, and distinguish themselves positively from others via their materialistic possessions. Our review lays the foundation for a novel methodological approach in which we explore how narcissism influences eye movement behavior during consumer decision-making. We conclude with a description of our experimental paradigm and report preliminary results. Our findings will provide insight into the mechanisms underlying narcissists’ conspicuous purchases. They will also likely have implications for theories of personality, consumer behavior, marketing, advertising, and visual cognition. PMID:24711797

Narcissism and consumer behaviour: a review and preliminary findings.

Science.gov (United States)

Cisek, Sylwia Z; Sedikides, Constantine; Hart, Claire M; Godwin, Hayward J; Benson, Valerie; Liversedge, Simon P

2014-01-01

We review the literature on the relation between narcissism and consumer behavior. Consumer behavior is sometimes guided by self-related motives (e.g., self-enhancement) rather than by rational economic considerations. Narcissism is a case in point. This personality trait reflects a self-centered, self-aggrandizing, dominant, and manipulative orientation. Narcissists are characterized by exhibitionism and vanity, and they see themselves as superior and entitled. To validate their grandiose self-image, narcissists purchase high-prestige products (i.e., luxurious, exclusive, flashy), show greater interest in the symbolic than utilitarian value of products, and distinguish themselves positively from others via their materialistic possessions. Our review lays the foundation for a novel methodological approach in which we explore how narcissism influences eye movement behavior during consumer decision-making. We conclude with a description of our experimental paradigm and report preliminary results. Our findings will provide insight into the mechanisms underlying narcissists' conspicuous purchases. They will also likely have implications for theories of personality, consumer behavior, marketing, advertising, and visual cognition.

Narcissism and Consumer Behaviour: A Review and Preliminary Findings

Directory of Open Access Journals (Sweden)

Sylwia Z Cisek

2014-03-01

Full Text Available We review the literature on the relation between narcissism and consumer behaviour. Consumer behaviour is sometimes guided by self-related motives (e.g., self-enhancement rather than by rational economic considerations. Narcissism is a case in point. This personality trait reflects a self-centred, self-aggrandizing, dominant, and manipulative orientation. Narcissists are characterised by exhibitionism and vanity, and they see themselves as superior and entitled. To validate their grandiose self-image, narcissists purchase high-prestige products (i.e., luxurious, exclusive, flashy, show greater interest in the symbolic than utilitarian value of products, and distinguish themselves positively from others via their materialistic possessions. Our review lays the foundation for a novel methodological approach in which we explore how narcissism influences eye movement behaviour during consumer decision-making. We conclude with a description of our experimental paradigm and report preliminary results. Our findings will provide insight into the mechanisms underlying narcissists’ conspicuous purchases. They will also likely have implications for theories of personality, consumer behaviour, marketing, advertising, and visual cognition.

Parents and Teachers‘ Voices of Quality Preschool: Preliminary findings from Indonesia

Directory of Open Access Journals (Sweden)

Edi Waluyo

2015-11-01

Full Text Available This paper describes preliminary findings of a study on Indonesian teachers and parents’ perspectives of quality preschool program. It departs in one hand from the context of the Indonesian government massive promotion of early childhood programs and on the other hand of the country top-down, government-dominated quality system. Moreover, it is contextualized within the growing body of literatures, which emphasizes the centrality of quality issues to early childhood service and the notion that quality is a complex, contextual, multifaceted construction and idea. This study found that even though parents and teachers’ constructions of quality share some commonalities with those of the government-constructed ones, they significantly differ. The government-constructed quality framework for example emphasizes on teacher formal qualification, but teachers and parents have moved beyond such formality and urged the importance of teacher personal character

75 FR 17161 - Job Corps: Preliminary Finding of No Significant Impact (FONSI) for the Installation of a Small...

Science.gov (United States)

2010-04-05

... DEPARTMENT OF LABOR Office of the Secretary Job Corps: Preliminary Finding of No Significant Impact (FONSI) for the Installation of a Small Wind Turbine at the Pine Ridge Job Corps Center Located at... the Pine Ridge Job Corps Center, 15710 Highway 385, Chadron, NE 69337. SUMMARY: Pursuant to the...

Comparison of logistic regression and neural models in predicting the outcome of biopsy in breast cancer from MRI findings

International Nuclear Information System (INIS)

Abdolmaleki, P.; Yarmohammadi, M.; Gity, M.

2004-01-01

Background: We designed an algorithmic model based on regression analysis and a non-algorithmic model based on the Artificial Neural Network. Materials and methods: The ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patient's records. Each patient's record consisted of 6 subjective features extracted from MRI appearance. These findings were enclosed as features extracted for an Artificial Neural Network as well as a logistic regression model to predict biopsy outcome. After both models had been trained perfectly on samples (n=100), the validation samples (n=61) were presented to the trained network as well as the established logistic regression models. Finally, the diagnostic performance of models were compared to the that of the radiologist in terms of sensitivity, specificity and accuracy, using receiver operating characteristic curve analysis. Results: The average out put of the Artificial Neural Network yielded a perfect sensitivity (98%) and high accuracy (90%) similar to that one of an expert radiologist (96% and 92%) while specificity was smaller than that (67%) verses 80%). The output of the logistic regression model using significant features showed improvement in specificity from 60% for the logistic regression model using all features to 93% for the reduced logistic regression model, keeping the accuracy around 90%. Conclusion: Results show that Artificial Neural Network and logistic regression model prove the relationship between extracted morphological features and biopsy results. Using statistically significant variables reduced logistic regression model outperformed of Artificial Neural Network with remarkable specificity while keeping high sensitivity is achieved

Cognitive remediation therapy for patients with anorexia nervosa: preliminary findings

Directory of Open Access Journals (Sweden)

Campbell Iain C

2007-06-01

Full Text Available Abstract Background Anorexia nervosa (AN is a severe mental illness. Drug treatments are not effective and there is no established first choice psychological treatment for adults with AN. Neuropsychological studies have shown that patients with AN have difficulties in cognitive flexibility: these laboratory based findings have been used to develop a clinical intervention based on Cognitive Remediation Therapy (CRT which aims to use cognitive exercises to strengthen thinking skills. Aims 1 To conduct a preliminary investigation of CRT in patients with AN 2 to explore whether cognitive training improves performance in set shifting tasks 3 to explore whether CRT exercises are appropriate and acceptable to AN patients 4 to use the data to improve a CRT module for AN patients. Methods Intervention was comprised of ten 45 minute sessions of CRT. Four patients with AN were assessed before and after the ten sessions using five set shifting tests and clinical assessments. At the end, each patient wrote a letter providing feedback on the intervention. Results Post intervention, three of the five set shifting assessments showed a moderate to large effect size in performance and two showed a large effect size in performance, both indicative of improved flexibility. Patients were aware of an improvement in their cognitive flexibility qualitative feedback was generally positive towards CRT. Discussion This preliminary study suggests that CRT changed performance on flexibility tasks and may be beneficial for acute, treatment resistant patients with AN. Feedback gathered from this small case series has enabled modification of the intervention for a future larger study, for example, by linking exercises with real life behavioural tasks and including exercises that encourage global thinking. Conclusion This exploratory study has produced encouraging data supporting the use of CRT in patients with AN: it has also provided insight into how the module should be

A phenomenographic investigation into Information Literacy in nursing practice - preliminary findings and methodological issues.

Science.gov (United States)

Forster, Marc

2013-10-01

Information Literacy is essential to 'evidence-based practice'; without the ability to locate evidence, evidence-based practice is rendered extremely difficult if not impossible. There is currently little evidence to show how Information Literacy is experienced by nurses or what its parameters are within evidence-based practice and therefore whether Information Literacy educational interventions are actually promoting the correct knowledge and skills. Using phenomenographic interviews the author will attempt to discover how nurses experience Information Literacy. Insights from the findings will be used to map out its parameters and to put forward a theoretical model for a course or module to develop it effectively. This article presents preliminary findings, including 7 draft categories of description of how Information Literacy is experienced in nursing. This pilot study indicates that the complete findings may be of significant potential value in the promotion and development of Information Literacy education in nursing. It is argued that such insights into how nurses actually experience the phenomenon of Information Literacy can be used to develop potentially more effective, research-based, educational interventions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Keeping rail on track: preliminary findings on safety culture in Australian rail.

Science.gov (United States)

Blewett, Verna; Rainbird, Sophia; Dorrian, Jill; Paterson, Jessica; Cattani, Marcus

2012-01-01

'Safety culture' is identified in the literature as a critical element of healthy and safe workplaces. How can rail organizations ensure that consistently effective work health and safety cultures are maintained across the diversity of their operations? This paper reports on research that is currently underway in the Australian rail industry aimed at producing a Model of Best Practice in Safety Culture for the industry. Located in rail organizations dedicated to the mining industry as well as urban rail and national freight operations, the research examines the constructs of organizational culture that impact on the development and maintenance of healthy and safe workplaces. The research uses a multi-method approach incorporating quantitative (survey) and qualitative (focus groups, interviews and document analysis) methods along with a participative process to identify interventions to improve the organization and develop plans for their implementation. The research uses as its analytical framework the 10 Platinum Rules, from the findings of earlier research in the New South Wales (Australia) mining industry, Digging Deeper. Data collection is underway at the time of writing and preliminary findings are presented at this stage. The research method may be adapted for use as a form of organizational review of safety and health in organizational culture.

Data Center Energy Efficiency Standards in India: Preliminary Findings from Global Practices

Energy Technology Data Exchange (ETDEWEB)

Raje, Sanyukta; Maan, Hermant; Ganguly, Suprotim; Singh, Tanvin; Jayaram, Nisha; Ghatikar, Girish; Greenberg, Steve; Kumar, Satish; Sartor, Dale

2015-06-01

Global data center energy consumption is growing rapidly. In India, information technology industry growth, fossil-fuel generation, and rising energy prices add significant operational costs and carbon emissions from energy-intensive data centers. Adoption of energy-efficient practices can improve the global competitiveness and sustainability of data centers in India. Previous studies have concluded that advancement of energy efficiency standards through policy and regulatory mechanisms is the fastest path to accelerate the adoption of energy-efficient practices in the Indian data centers. In this study, we reviewed data center energy efficiency practices in the United States, Europe, and Asia. Using evaluation metrics, we identified an initial set of energy efficiency standards applicable to the Indian context using the existing policy mechanisms. These preliminary findings support next steps to recommend energy efficiency standards and inform policy makers on strategies to adopt energy-efficient technologies and practices in Indian data centers.

HPA-axis hyperactivity and mortality in psychotic depressive disorder: preliminary findings.

Science.gov (United States)

Coryell, William; Fiedorowicz, Jess; Zimmerman, Mark; Young, Elizabeth

2008-06-01

The excess mortality associated with depressive disorders has been most often attributed to risks for suicide but diverse findings indicate that depressive disorders also increase risks for cardiovascular (CV) mortality. Among the possible mediators is the hypothalamic-pituitary-adrenal (HPA)-axis hyperactivity that characterizes many cases of relatively severe depressive disorder and severity is characteristic of psychotic depressive disorder. The following describes a 17-year mortality follow-up of 54 patients with Research Diagnostic Criteria (RDC) psychotic major depression or schizoaffective, mainly affective, depression. All had baseline assessments that included a 1mg dexamethasone suppression test with post-dexamethasone samples at 8 a.m., 4 p.m. and 11 p.m. Regression analyses showed that both greater age and higher maximum post-dexamethasone cortisol concentrations predicted deaths due to CV causes (t=4.01, pdepressive disorder to CV mortality.

Semiparametric regression during 2003–2007

KAUST Repository

Ruppert, David; Wand, M.P.; Carroll, Raymond J.

2009-01-01

Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.

Attachment and Aggressive Manifestations in Younger Adulthood - "Preliminary Findings"

Directory of Open Access Journals (Sweden)

Tatiana Lorincová

2014-07-01

Full Text Available The main topic of the contribution was comparison between retrospective attachment (emocional warmth and rejection and aggressive manifestations (physical aggressivness, verbal aggressivness, anger and hostility among younger adulthood. Bowlby's theory of attachment was that once a core attachment style develops in an infant, it will influence and shape the nature of all intimate relations for the individual moving forward throughout the infant's life cycle. Authors Mikulincer and Shaver (2011 explain how these primary attachment experiences would affect future emotional, cognitive and behavioral processes. Secure adolescents, in comparison to insecure ones are perceived as being less aggressive. Research has pointed out that secure parental attachment promotes adaptive psychological functioning. The direct relationship between attachment security and aggressive/delinquent behaviour is in line with prior evidence that secure adolescents rate higher in terms of emotional and social adjustment, enjoy more positive relationships with their family and peers, and are less likely to engage in externalizing problems, such as antisocial and aggressive behaviours. On the other hand, insecure attachment is connected with aggressive and externalizing behaviour. Hypotheses were formulated on the base of theoretical background and our assumption was, that younger adults with emocional warmth attachment will have lower level of aggressive manifestations (physical aggression, verbal aggression, anger and hostility than younger adults with rejectional attachment. We used two standardized questionnaires for data collection, s.E.M.B.U. Questionnaire, which measured retrospective attachment (emocional warmth and rejection and Questionnaire of Aggressivness, which measured aggressive manifestations. We used statistical analysis and we found statistically significant differencies, which are preliminary findings from broader research, between emocional warmth

Radical university-industry innovation – research design and preliminary findings from an on-going qualitative case study

DEFF Research Database (Denmark)

Gertsen, Frank; Nielsen, René Nesgaard

and it is arguing that there is a lack of in-depth understanding of such collaborative radical innovation processes. The paper then suggests an abductive research design for an explorative in-depth case study of collaborative radical innovation involving a university and an established Danish manufacturing firm....... Some preliminary findings are presented and briefly discussed, including the role of the university’s formal set-up to deal with IPR/commercialisation and the researchers’ personal networking with industry as well as challenges concerning the sharing of IPR/commercialisation outcomes....

Psychophysiological deficits in young adolescents with psychosis or ADHD: Preliminary findings

DEFF Research Database (Denmark)

Rydkjær, Jacob; Jepsen, Jens Richardt Møllegaard; Fagerlund, Birgitte

add valuable information on how to differentiate premature stages of early onset psychosis from ADHD. Aim: To characterize psychophysiological deficits in young adolescents with psychosis or ADHD and compare the profiles of impariments between the two groups. Materials and methods: A cohort of young...... and low intensity prepulse trials, Mismatch Negativity (MMN), Selective Attention (SA) and P50. Results: Preliminary analyses of 18 patients with psychosis and 12 patients with ADHD showed significantly less PPI in the higher intensity prepulse trials in the psychosis group than in the ADHD group....... No significant group difference was found in the lower intensity prepulse trials. Conclusion: The preliminary results indicate lower levels of PPI in adolescents with early onset psychosis than in young patients with ADHD. If these results hold in the final analyses then this knowledge may contribute to better...

Experimental interstellar organic chemistry - Preliminary findings

Science.gov (United States)

Khare, B. N.; Sagan, C.

1973-01-01

Review of the results of some explicit experimental simulation of interstellar organic chemistry consisting in low-temperature high-vacuum UV irradiation of condensed simple gases known or suspected to be present in the interstellar medium. The results include the finding that acetonitrile may be present in the interstellar medium. The implication of this and other findings are discussed.

Finding determinants of audit delay by pooled OLS regression analysis

OpenAIRE

Vuko, Tina; Čular, Marko

2014-01-01

The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days) from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011). We use pooled OLS regression analysis, mode...

Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India

Directory of Open Access Journals (Sweden)

Michael L. Mann

2016-08-01

Full Text Available Unreliable electricity supplies are common in developing countries and impose large socio-economic costs, yet precise information on electricity reliability is typically unavailable. This paper presents preliminary results from a machine-learning approach for using satellite imagery of nighttime lights to develop estimates of electricity reliability for western India at a finer spatial scale. We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS onboard the Suomi National Polar Partnership (SNPP satellite together with newly-available data from networked household voltage meters. Our results point to the possibilities of this approach as well as areas for refinement. With currently available training data, we find a limited ability to detect individual outages identified by household-level measurements of electricity voltage. This is likely due to the relatively small number of individual outages observed in our preliminary data. However, we find that the approach can estimate electricity reliability rates for individual locations fairly well, with the predicted versus actual regression yielding an R2 > 0.5. We also find that, despite the after midnight overpass time of the SNPP satellite, the reliability estimates derived are representative of daytime reliability.

Exponential Decay Nonlinear Regression Analysis of Patient Survival Curves: Preliminary Assessment in Non-Small Cell Lung Cancer

Science.gov (United States)

Stewart, David J.; Behrens, Carmen; Roth, Jack; Wistuba, Ignacio I.

2010-01-01

Background For processes that follow first order kinetics, exponential decay nonlinear regression analysis (EDNRA) may delineate curve characteristics and suggest processes affecting curve shape. We conducted a preliminary feasibility assessment of EDNRA of patient survival curves. Methods EDNRA was performed on Kaplan-Meier overall survival (OS) and time-to-relapse (TTR) curves for 323 patients with resected NSCLC and on OS and progression-free survival (PFS) curves from selected publications. Results and Conclusions In our resected patients, TTR curves were triphasic with a “cured” fraction of 60.7% (half-life [t1/2] >100,000 months), a rapidly-relapsing group (7.4%, t1/2=5.9 months) and a slowly-relapsing group (31.9%, t1/2=23.6 months). OS was uniphasic (t1/2=74.3 months), suggesting an impact of co-morbidities; hence, tumor molecular characteristics would more likely predict TTR than OS. Of 172 published curves analyzed, 72 (42%) were uniphasic, 92 (53%) were biphasic, 8 (5%) were triphasic. With first-line chemotherapy in advanced NSCLC, 87.5% of curves from 2-3 drug regimens were uniphasic vs only 20% of those with best supportive care or 1 drug (p<0.001). 54% of curves from 2-3 drug regimens had convex rapid-decay phases vs 0% with fewer agents (p<0.001). Curve convexities suggest that discontinuing chemotherapy after 3-6 cycles “synchronizes” patient progression and death. With postoperative adjuvant chemotherapy, the PFS rapid-decay phase accounted for a smaller proportion of the population than in controls (p=0.02) with no significant difference in rapid-decay t1/2, suggesting adjuvant chemotherapy may move a subpopulation of patients with sensitive tumors from the relapsing group to the cured group, with minimal impact on time to relapse for a larger group of patients with resistant tumors. In untreated patients, the proportion of patients in the rapid-decay phase increased (p=0.04) while rapid-decay t1/2 decreased (p=0.0004) with increasing

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...

Towards Finding the Global Minimum of the D-Wave Objective Function for Improved Neural Network Regressions

Science.gov (United States)

Dorband, J. E.

2017-12-01

The D-Wave 2X has successfully been used for regression analysis to derive carbon flux data from OCO-2 CO2 concentration using neural networks. The samples returned from the D-Wave should represent the minimum of an objective function presented to it. An accurate as possible minimum function value is needed for this analysis. Samples from the D-Wave are near minimum, but seldom are the global minimum of the function due to quantum noise. Two methods for improving the accuracy of minimized values represented by the samples returned from the D-Wave are presented. The first method finds a new sample with a minimum value near each returned D-Wave sample. The second method uses all the returned samples to find a more global minimum sample. We present three use-cases performed using the former method. In the first use case, it is demonstrated that an objective function with random qubits and coupler coefficients had an improved minimum. In the second use case, the samples corrected by the first method can improve the training of a Boltzmann machine neural network. The third use case demonstrated that using the first method can improve virtual qubit accuracy.The later method was also performed on the first use case.

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...

Anatomy and histology as socially networked learning environments: some preliminary findings.

Science.gov (United States)

Hafferty, Frederic W; Castellani, Brian; Hafferty, Philip K; Pawlina, Wojciech

2013-09-01

An exploratory study to better understand the "networked" life of the medical school as a learning environment. In a recent academic year, the authors gathered data during two six-week blocks of a sequential histology and anatomy course at a U.S. medical college. An eight-item questionnaire captured different dimensions of student interactions. The student cohort/network was 48 first-year medical students. Using social network analysis (SNA), the authors focused on (1) the initial structure and the evolution of informal class networks over time, (2) how informal class networks compare to formal in-class small-group assignments in influencing student information gathering, and (3) how peer assignment of professionalism role model status is shaped more by informal than formal ties. In examining these latter two issues, the authors explored not only how formal group assignment persisted over time but also how it functioned to prevent the tendency for groupings based on gender or ethnicity. The study revealed an evolving dynamic between the formal small-group learning structure of the course blocks and the emergence of informal student networks. For example, whereas formal group membership did influence in-class questions and did prevent formation of groups of like gender and ethnicity, outside-class questions and professionalism were influenced more by informal group ties where gender and, to a much lesser extent, ethnicity influence student information gathering. The richness of these preliminary findings suggests that SNA may be a useful tool in examining an array of medical student learning encounters.

Finding determinants of audit delay by pooled OLS regression analysis

Directory of Open Access Journals (Sweden)

Tina Vuko

2014-03-01

Full Text Available The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011. We use pooled OLS regression analysis, modelling audit delay as a function of the following explanatory variables: audit firm type, audit opinion, profitability, leverage, inventory and receivables to total assets, absolute value of total accruals, company size and audit committee existence. Our results indicate that audit committee existence, profitability and leverage are statistically significant determinants of audit delay in Croatia.

Lingual-Alveolar Contact Pressure during Speech in Amyotrophic Lateral Sclerosis: Preliminary Findings

Science.gov (United States)

Searl, Jeff; Knollhoff, Stephanie; Barohn, Richard J.

2017-01-01

Purpose: This preliminary study on lingual-alveolar contact pressures (LACP) in people with amyotrophic lateral sclerosis (ALS) had several aims: (a) to evaluate whether the protocol induced fatigue, (b) to compare LACP during speech (LACP-Sp) and during maximum isometric pressing (LACP-Max) in people with ALS (PALS) versus healthy controls, (c)…

A simplified procedure of linear regression in a preliminary analysis

Directory of Open Access Journals (Sweden)

Silvia Facchinetti

2013-05-01

Full Text Available The analysis of a statistical large data-set can be led by the study of a particularly interesting variable Y – regressed – and an explicative variable X, chosen among the remained variables, conjointly observed. The study gives a simplified procedure to obtain the functional link of the variables y=y(x by a partition of the data-set into m subsets, in which the observations are synthesized by location indices (mean or median of X and Y. Polynomial models for y(x of order r are considered to verify the characteristics of the given procedure, in particular we assume r= 1 and 2. The distributions of the parameter estimators are obtained by simulation, when the fitting is done for m= r + 1. Comparisons of the results, in terms of distribution and efficiency, are made with the results obtained by the ordinary least square methods. The study also gives some considerations on the consistency of the estimated parameters obtained by the given procedure.

Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

Energy Technology Data Exchange (ETDEWEB)

Deng, Yangyang; Parajuli, Prem B.

2011-08-10

Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

DYNA3D/ParaDyn Regression Test Suite Inventory

Energy Technology Data Exchange (ETDEWEB)

Lin, Jerry I. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-09-01

The following table constitutes an initial assessment of feature coverage across the regression test suite used for DYNA3D and ParaDyn. It documents the regression test suite at the time of preliminary release 16.1 in September 2016. The columns of the table represent groupings of functionalities, e.g., material models. Each problem in the test suite is represented by a row in the table. All features exercised by the problem are denoted by a check mark (√) in the corresponding column. The definition of “feature” has not been subdivided to its smallest unit of user input, e.g., algorithmic parameters specific to a particular type of contact surface. This represents a judgment to provide code developers and users a reasonable impression of feature coverage without expanding the width of the table by several multiples. All regression testing is run in parallel, typically with eight processors, except problems involving features only available in serial mode. Many are strictly regression tests acting as a check that the codes continue to produce adequately repeatable results as development unfolds; compilers change and platforms are replaced. A subset of the tests represents true verification problems that have been checked against analytical or other benchmark solutions. Users are welcomed to submit documented problems for inclusion in the test suite, especially if they are heavily exercising, and dependent upon, features that are currently underrepresented.

An Investigation of the Academic Information Finding and Re-finding Behavior on the Web

Directory of Open Access Journals (Sweden)

Hsiao-Tieh Pu

2014-12-01

Full Text Available Academic researchers often need and re-use relevant information found after a period of time. This preliminary study used various methods, including experiments, interviews, search log analysis, sequential analysis, and observation to investigate characteristics of academic information finding and re-finding behavior. Overall, the participants in this study entered short queries either in finding or re-finding phases. Comparatively speaking, the participants entered greater number of queries, modified more queries, browsed more web pages, and stayed longer on web pages in the finding phase. On the other hand, in the re-finding phase, they utilized personal information management tools to re-find instead of finding again using search engine, such as checking browsing history; moreover, they tend to input less number of queries and stayed shorter on web pages. In short, the participants interacted more with the retrieval system during the finding phase, while they increased the use of personal information management tools in the re-finding phase. As to the contextual clues used in re-finding phase, the participants used less clues from the target itself, instead, they used indirect clues more often, especially location-related information. Based on the results of sequential analysis, the transition states in the re-finding phase was found to be more complex than those in the finding phase. Web information finding and re-finding behavior is an important and novel area of research. The preliminary results would benefit research on Web information re-finding behavior, and provide useful suggestions for developing personal academic information management systems. [Article content in Chinese

Quasi-experimental evidence on tobacco tax regressivity.

Science.gov (United States)

Koch, Steven F

2018-01-01

Tobacco taxes are known to reduce tobacco consumption and to be regressive, such that tobacco control policy may have the perverse effect of further harming the poor. However, if tobacco consumption falls faster amongst the poor than the rich, tobacco control policy can actually be progressive. We take advantage of persistent and committed tobacco control activities in South Africa to examine the household tobacco expenditure burden. For the analysis, we make use of two South African Income and Expenditure Surveys (2005/06 and 2010/11) that span a series of such tax increases and have been matched across the years, yielding 7806 matched pairs of tobacco consuming households and 4909 matched pairs of cigarette consuming households. By matching households across the surveys, we are able to examine both the regressivity of the household tobacco burden, and any change in that regressivity, and since tobacco taxes have been a consistent component of tobacco prices, our results also relate to the regressivity of tobacco taxes. Like previous research into cigarette and tobacco expenditures, we find that the tobacco burden is regressive; thus, so are tobacco taxes. However, we find that over the five-year period considered, the tobacco burden has decreased, and, most importantly, falls less heavily on the poor. Thus, the tobacco burden and the tobacco tax is less regressive in 2010/11 than in 2005/06. Thus, increased tobacco taxes can, in at least some circumstances, reduce the financial burden that tobacco places on households. Copyright © 2017 Elsevier Ltd. All rights reserved.

Finding Silver Linings: A Preliminary Examination of Benefit Finding in Youth With Chronic Pain.

Science.gov (United States)

Soltani, Sabine; Neville, Alex; Hurtubise, Karen; Hildenbrand, Aimee; Noel, Melanie

2018-04-01

Chronic pain is a pervasive condition in adolescence and is associated with significant psychological distress, functional disability, social isolation, and decreased quality of life for a subset of affected youth. There is a paucity of research examining potential resilience factors and adaptive processes in pediatric chronic pain. Benefit finding refers to the process of perceiving positive consequences in the face of adversity. Previous research on benefit finding in pediatric samples (e.g., oncology; acute injury) has yielded inconsistent results. This is the first study to examine this construct in youth with chronic pain. The objective of the current investigation was to extend previous research on benefit finding to adolescents with chronic pain and to assess relationships between benefit finding, internalizing mental health symptoms (i.e., anxiety, depression, and posttraumatic stress disorder [PTSD]), pain outcomes (pain intensity and interference), and quality of life. Psychometrically sound self-report measures of benefit finding, anxiety, depressive, and PTSD symptoms, pain intensity, pain interference, and quality of life were completed by 145 youth (67.4% female, Mage = 13.3 years, SD = 2.6), referred to a tertiary-level chronic pain program. Benefit finding was significantly correlated with internalizing mental health symptoms, pain outcomes, and quality of life. Further, benefit finding significantly predicted children's self-reported pain intensity, pain interference, and quality of life when controlling for age and sex. Findings suggest that benefit finding is associated with internalizing mental health symptoms, pain outcomes, and quality of life in youth with chronic pain. Future research examining this construct is warranted.

Variable and subset selection in PLS regression

DEFF Research Database (Denmark)

Høskuldsson, Agnar

2001-01-01

The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...

Retro-regression--another important multivariate regression improvement.

Science.gov (United States)

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Modified Regression Correlation Coefficient for Poisson Regression Model

Science.gov (United States)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

A preliminary study to find out maximum occlusal bite force in Indian individuals

DEFF Research Database (Denmark)

Jain, Veena; Mathur, Vijay Prakash; Pillai, Rajath

2014-01-01

PURPOSE: This preliminary hospital based study was designed to measure the mean maximum bite force (MMBF) in healthy Indian individuals. An attempt was made to correlate MMBF with body mass index (BMI) and some of the anthropometric features. METHODOLOGY: A total of 358 healthy subjects in the ag...

Canine-assisted therapy for children with ADHD: preliminary findings from the positive assertive cooperative kids study.

Science.gov (United States)

Schuck, Sabrina E B; Emmerson, Natasha A; Fine, Aubrey H; Lakes, Kimberley D

2015-02-01

The objective of this study was to provide preliminary findings from an ongoing randomized clinical trial using a canine-assisted intervention (CAI) for 24 children with ADHD. Project Positive Assertive Cooperative Kids (P.A.C.K.) was designed to study a 12-week cognitive-behavioral intervention delivered with or without CAI. Children were randomly assigned to group therapy with or without CAI. Parents of children in both groups simultaneously participated in weekly parent group therapy sessions. Across both treatment groups, parents reported improvements in children's social skills, prosocial behaviors, and problematic behaviors. In both groups, the severity of ADHD symptoms declined during the course of treatment; however, children who received the CAI model exhibited greater reductions in the severity of ADHD symptoms than did children who received cognitive-behavioral therapy without CAI. Results suggest that CAI offers a novel therapeutic strategy that may enhance cognitive-behavioral interventions for children with ADHD. © 2013 SAGE Publications.

Preliminary Findings from an Analysis of Building Energy Information System Technologies

Energy Technology Data Exchange (ETDEWEB)

Granderson, Jessica; Piette, Mary Ann; Ghatikar, Girish; Price, Philip

2009-06-01

Energy information systems comprise software, data acquisition hardware, and communication systems that are intended to provide energy information to building energy and facilities managers, financial managers, and utilities. This technology has been commercially available for over a decade, however recent advances in Internet and other information technology, and analytical features have expanded the number of product options that are available. For example, features such as green house gas tracking, configurable energy analyses and enhanced interoperability are becoming increasingly common. Energy information systems are used in a variety of commercial buildings operations and environments, and can be characterized in a number of ways. Basic elements of these systems include web-based energy monitoring, web-based energy management linked to controls, demand response, and enterprise energy management applications. However the sheer number and variety of available systems complicate the selection of products to match the needs of a given user. In response, a framework was developed to define the capabilities of different types of energy information systems, and was applied to characterize approximately 30 technologies. Measurement is a critical component in managing energy consumption and energy information must be shared at all organizational levels to maintain persistent, efficient operations. Energy information systems are important to understand because they offer the analytical support to process measured data into information, and they provide the informational link between the primary actors who impact building energy efficiency - operators, facilities and energy managers, owners and corporate decision makers. In this paper, preliminary findings are presented, with a focus on overall trends and the general state of the technology. Key conclusions include the need to further pursue standardization and usability, x-y plotting as an under-supported feature, and

Landslide Hazard Mapping in Rwanda Using Logistic Regression

Science.gov (United States)

Piller, A.; Anderson, E.; Ballard, H.

2015-12-01

Landslides in the United States cause more than $1 billion in damages and 50 deaths per year (USGS 2014). Globally, figures are much more grave, yet monitoring, mapping and forecasting of these hazards are less than adequate. Seventy-five percent of the population of Rwanda earns a living from farming, mostly subsistence. Loss of farmland, housing, or life, to landslides is a very real hazard. Landslides in Rwanda have an impact at the economic, social, and environmental level. In a developing nation that faces challenges in tracking, cataloging, and predicting the numerous landslides that occur each year, satellite imagery and spatial analysis allow for remote study. We have focused on the development of a landslide inventory and a statistical methodology for assessing landslide hazards. Using logistic regression on approximately 30 test variables (i.e. slope, soil type, land cover, etc.) and a sample of over 200 landslides, we determine which variables are statistically most relevant to landslide occurrence in Rwanda. A preliminary predictive hazard map for Rwanda has been produced, using the variables selected from the logistic regression analysis.

MEASURING PLACE ATTACHMENT TO CĂLIMANI NATIONAL PARK (ROMANIA AMONG LOCAL RESIDENTS AND TOURISTS. PRELIMINARY FINDINGS

Directory of Open Access Journals (Sweden)

IZABELA AMALIA MIHALCA

2014-06-01

Full Text Available Measuring Place Attachment to Călimani National Park (Romania among Local Residents and Tourists. Preliminary Findings. Understanding the attachments that people develop for certain places is an interesting area of study, but with little attention in Romanian empirical research. This study introduces the concepts of place identity and place dependence in relation to a specific area within the local culture of the Land of Dorna. Starting from previous studies carried on western samples, a research instrument measuring the degree and content of place attachment was translated and adapted. 86 respondents (52 residents and 34 tourists filled in the questionnaire. Comparing the degree of attachment, there was no significant difference among the two samples. However, local residents tended to display higher levels of place identity, while tourists displayed more emotional functionality to the study area. Place attachment is deeply embodied in the local culture. Due to the research design the generalization of the results is limited. However, this study may act as a starting point in researching other geographical mental spaces. The lands of Romania are unique social and cultural spaces with specific attachment patterns. Future studies should consider larger and representative samples in order to find additional patterns of attachment among residents and other individuals (e.g. tourists, visitors, new residents, other communities etc..

Preliminary study of image findings of femoroacetabular impingement

International Nuclear Information System (INIS)

Guo Zhe; Zhang Jing; Hong Nan; Cheng Xiaoguang

2010-01-01

Objective: To assess the image findings of femoroacetabular impingement (FAI). Methods: Image findings of 9 patients with surgically proved femoroacetabular impingement were retrospectively reviewed for characteristic image findings of FAI. All 9 patients underwent X-ray examinations and MRI of affected hip, and 1 patient underwent MR arthrography (MRA) additionally. Results: X-ray examinations of all 9 patients showed bump at femoral head-neck junction or overcoverage of the acetabular. MRI showed various degrees of injury of anterosuperior labrum in all 9 patients. The injuries were stage Ⅰ A in 2 cases, stage Ⅰ B in 3, stage Ⅱ A in 2, and stage Ⅱ B in 2. MRA of the case showed tears of anterosuperior labrum, with contrast media entering into the teared labrum. There were sclerosis and cystic degeneration of subchondral bone of femoral head in 2 cases, and these findings were confirmed as cartilage delamination by surgery. Conclusions: MRI can display the injures of labrum and articular cartilage, which is helpful to the early diagnosis of' FAI. (authors)

A Brief Report: Lessons Learned and Preliminary Findings of Progreso en Salud, an HIV Risk Reduction Intervention for Latina Seasonal Farmworkers.

Science.gov (United States)

Kanamori, Mariano; De La Rosa, Mario; Diez, Stephanie; Weissman, Jessica; Trepka, Mary Jo; Sneij, Alicia; Schmidt, Peter; Rojas, Patria

2016-12-30

Throughout the past decade, HIV rates in Florida-particularly South Florida, where many Latina seasonal farmworkers reside and work-have ranked among the highest in the nation. In this brief report, we delineate important lessons learned and preliminary findings from the implementation of the HIV prevention intervention Progreso en Salud (Progress in Health). Among the 114 Latina seasonal farmworker participants, there were significant increases from baseline to 6-month follow-up in the percentages of overall condom use, HIV testing, HIV/AIDS-related communications with friends, HIV knowledge, condom use self-efficacy, and correct use of condoms. Lessons learned from this study can be used to inform future HIV intervention strategies to improve the adoption and maintenance of HIV risk reduction behaviors among high-risk Latina seasonal workers and other high-risk underserved populations. Future research is needed to support our findings.

Retrospective study of sonographic findings in bone involvement associated with rotator cuff calcific tendinopathy: preliminary results of a case series

Directory of Open Access Journals (Sweden)

Marcello H. Nogueira-Barbosa

2015-12-01

Full Text Available Abstract Objective: The present study was aimed at investigating bone involvement secondary to rotator cuff calcific tendonitis at ultrasonography. Materials and Methods: Retrospective study of a case series. The authors reviewed shoulder ultrasonography reports of 141 patients diagnosed with rotator cuff calcific tendonitis, collected from the computer-based data records of their institution over a four-year period. Imaging findings were retrospectively and consensually analyzed by two experienced musculoskeletal radiologists looking for bone involvement associated with calcific tendonitis. Only the cases confirmed by computed tomography were considered for descriptive analysis. Results: Sonographic findings of calcific tendinopathy with bone involvement were observed in 7/141 (~ 5% patients (mean age, 50.9 years; age range, 42-58 years; 42% female. Cortical bone erosion adjacent to tendon calcification was the most common finding, observed in 7/7 cases. Signs of intraosseous migration were found in 3/7 cases, and subcortical cysts in 2/7 cases. The findings were confirmed by computed tomography. Calcifications associated with bone abnormalities showed no acoustic shadowing at ultrasonography, favoring the hypothesis of resorption phase of the disease. Conclusion: Preliminary results of the present study suggest that ultrasonography can identify bone abnormalities secondary to rotator cuff calcific tendinopathy, particularly the presence of cortical bone erosion.

Reducing false-positive incidental findings with ensemble genotyping and logistic regression based variant filtering methods.

Science.gov (United States)

Hwang, Kyu-Baek; Lee, In-Hee; Park, Jin-Ho; Hambuch, Tina; Choe, Yongjoon; Kim, MinHyeok; Lee, Kyungjoon; Song, Taemin; Neu, Matthew B; Gupta, Neha; Kohane, Isaac S; Green, Robert C; Kong, Sek Won

2014-08-01

As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false-positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here, we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false-negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous single nucleotide variants (SNVs); 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery in NA12878, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and an ensemble genotyping would be essential to minimize false-positive DNM candidates. © 2014 WILEY PERIODICALS, INC.

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...

A Brief Report: Lessons Learned and Preliminary Findings of Progreso en Salud, an HIV Risk Reduction Intervention for Latina Seasonal Farmworkers

Directory of Open Access Journals (Sweden)

Mariano Kanamori

2016-12-01

Full Text Available Throughout the past decade, HIV rates in Florida—particularly South Florida, where many Latina seasonal farmworkers reside and work—have ranked among the highest in the nation. In this brief report, we delineate important lessons learned and preliminary findings from the implementation of the HIV prevention intervention Progreso en Salud (Progress in Health. Among the 114 Latina seasonal farmworker participants, there were significant increases from baseline to 6-month follow-up in the percentages of overall condom use, HIV testing, HIV/AIDS-related communications with friends, HIV knowledge, condom use self-efficacy, and correct use of condoms. Lessons learned from this study can be used to inform future HIV intervention strategies to improve the adoption and maintenance of HIV risk reduction behaviors among high-risk Latina seasonal workers and other high-risk underserved populations. Future research is needed to support our findings.

Prediction accuracy and stability of regression with optimal scaling transformations

NARCIS (Netherlands)

Kooij, van der Anita J.

2007-01-01

The central topic of this thesis is the CATREG approach to nonlinear regression. This approach finds optimal quantifications for categorical variables and/or nonlinear transformations for numerical variables in regression analysis. (CATREG is implemented in SPSS Categories by the author of the

Five cases of caudal regression with an aberrant abdominal umbilical artery: Further support for a caudal regression-sirenomelia spectrum.

Science.gov (United States)

Duesterhoeft, Sara M; Ernst, Linda M; Siebert, Joseph R; Kapur, Raj P

2007-12-15

Sirenomelia and caudal regression have sparked centuries of interest and recent debate regarding their classification and pathogenetic relationship. Specific anomalies are common to both conditions, but aside from fusion of the lower extremities, an aberrant abdominal umbilical artery ("persistent vitelline artery") has been invoked as the chief anatomic finding that distinguishes sirenomelia from caudal regression. This observation is important from a pathogenetic viewpoint, in that diversion of blood away from the caudal portion of the embryo through the abdominal umbilical artery ("vascular steal") has been proposed as the primary mechanism leading to sirenomelia. In contrast, caudal regression is hypothesized to arise from primary deficiency of caudal mesoderm. We present five cases of caudal regression that exhibit an aberrant abdominal umbilical artery similar to that typically associated with sirenomelia. Review of the literature identified four similar cases. Collectively, the series lends support for a caudal regression-sirenomelia spectrum with a common pathogenetic basis and suggests that abnormal umbilical arterial anatomy may be the consequence, rather than the cause, of deficient caudal mesoderm. (c) 2007 Wiley-Liss, Inc.

Preliminary findings of altered functional connectivity of the default mode network linked to functional outcomes one year after pediatric traumatic brain injury.

Science.gov (United States)

Stephens, Jaclyn A; Salorio, Cynthia F; Barber, Anita D; Risen, Sarah R; Mostofsky, Stewart H; Suskauer, Stacy J

2017-07-10

This study examined functional connectivity of the default mode network (DMN) and examined brain-behavior relationships in a pilot cohort of children with chronic mild to moderate traumatic brain injury (TBI). Compared to uninjured peers, children with TBI demonstrated less anti-correlated functional connectivity between DMN and right Brodmann Area 40 (BA 40). In children with TBI, more anomalous less anti-correlated) connectivity between DMN and right BA 40 was linked to poorer performance on response inhibition tasks. Collectively, these preliminary findings suggest that functional connectivity between DMN and BA 40 may relate to longterm functional outcomes in chronic pediatric TBI.

Alternative Health Care Practitioners in a Chinese American Community: A Preliminary Report of Findings.

Science.gov (United States)

Kao, Jessica Ching-Yi

This paper provides a brief review of the literature on traditional Chinese medicine in both China and the United States and presents observations from a preliminary study of Chinese practitioners in the Chinatown section of Los Angeles, California. The dualistic health care system in Chinese culture is described as comprising both scholarly and…

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.…

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...

Advanced statistics: linear regression, part I: simple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Assessing health-related resiliency in HIV+ Latin women: Preliminary psychometric findings.

Directory of Open Access Journals (Sweden)

Gladys J Jimenez-Torres

Full Text Available HIV-associated vulnerabilities-especially those linked to psychological issues-and limited mental health-treatment resources have the potential to adversely affect the health statuses of individuals. The concept of resilience has been introduced in the literature to shift the emphasis from vulnerability to protective factors. Resilience, however, is an evolving construct and is measured in various ways, though rarely among underserved, minority populations. Herein, we present the preliminary psychometric properties of a sample of HIV-seropositive Puerto Rican women, measured using a newly developed health-related resilience scale.The Resilience Scales for Children and Adolescents, an instrument with solid test construction properties, acted as a model in the development (in both English and Spanish of the HRRS, providing the same dimensions and most of the same subscales. The present sample was nested within the Hispanic-Latino longitudinal cohort of women (HLLC, that is part of the NeuroAIDS Research Program at the University of Puerto Rico (UPR, Medical Sciences Campus (MSC. Forty-five consecutively recruited, HIV+ women from the HLLC completed a demographic survey, the HRRS, and the Beck Depression Inventory-I, Spanish version.The results demonstrate excellent overall internal consistency for the total HRRS score (α = 0.95. Each of the dimensional scores also evidenced acceptable internal consistency (α ≥ 0.88. All the dimensional and subscale content validity indices were above the 0.42 cut-off. Analysis revealed a significant negative correlation between the HRRS total score and BDI-I-S (r(45 = -0.453, p < 0.003.Albeit preliminary in nature, the present study provides support for the HRRS as a measure to assess resilience among individuals living with chronic medical conditions. Minority populations, especially non-English speaking ones, are understudied across the field of medicine, and when efforts are made to include these patient

Preliminary bioelectrical impedance analysis (BIA) equation for body composition assessment in young females from Colombia

International Nuclear Information System (INIS)

Caicedo, J C; González-Correa, C H; González-Correa, C A

2013-01-01

A previous study showed that reported BIA equations for body composition are not suitable for Colombian population. The purpose of this study was to develop and validate a preliminary BIA equation for body composition assessment in young females from Colombia, using hydrodensitometry as reference method. A sample of 30 young females was evaluated. Inclusion and exclusion criteria were defined to minimize the variability of BIA. Height, weight, BIA, residual lung volume (RV) and underwater weight (UWW) were measured. A preliminary BIA equation was developed (r 2 = 0.72, SEE = 2.48 kg) by stepwise multiple regression with fat-free mass (FFM) as dependent variable and weight, height and impedance measurements as independent variables. The quality of regression was evaluated and a cross-validation against 50% of sample confirmed that results obtained with the preliminary BIA equation is interchangeable with results obtained with hydrodensitometry (r 2 = 0.84, SEE = 2.62 kg). The preliminary BIA equation can be used for body composition assessment in young females from Colombia until a definitive equation is developed. The next step will be increasing the sample, including a second reference method, as deuterium oxide dilution (D 2 O), and using multi-frequency BIA (MF-BIA). It would also be desirable to develop equations for males and other ethnic groups in Colombia.

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

Science.gov (United States)

Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H

2016-04-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

Logistic regression analysis of psychosocial correlates associated with recovery from schizophrenia in a Chinese community.

Science.gov (United States)

Tse, Samson; Davidson, Larry; Chung, Ka-Fai; Yu, Chong Ho; Ng, King Lam; Tsoi, Emily

2015-02-01

More mental health services are adopting the recovery paradigm. This study adds to prior research by (a) using measures of stages of recovery and elements of recovery that were designed and validated in a non-Western, Chinese culture and (b) testing which demographic factors predict advanced recovery and whether placing importance on certain elements predicts advanced recovery. We examined recovery and factors associated with recovery among 75 Hong Kong adults who were diagnosed with schizophrenia and assessed to be in clinical remission. Data were collected on socio-demographic factors, recovery stages and elements associated with recovery. Logistic regression analysis was used to identify variables that could best predict stages of recovery. Receiver operating characteristic curves were used to detect the classification accuracy of the model (i.e. rates of correct classification of stages of recovery). Logistic regression results indicated that stages of recovery could be distinguished with reasonable accuracy for Stage 3 ('living with disability', classification accuracy = 75.45%) and Stage 4 ('living beyond disability', classification accuracy = 75.50%). However, there was no sufficient information to predict Combined Stages 1 and 2 ('overwhelmed by disability' and 'struggling with disability'). It was found that having a meaningful role and age were the most important differentiators of recovery stage. Preliminary findings suggest that adopting salient life roles personally is important to recovery and that this component should be incorporated into mental health services. © The Author(s) 2014.

Cognitive Task Analysis of Business Jet Pilots' Weather Flying Behaviors: Preliminary Results

Science.gov (United States)

Latorella, Kara; Pliske, Rebecca; Hutton, Robert; Chrenka, Jason

2001-01-01

This report presents preliminary findings from a cognitive task analysis (CTA) of business aviation piloting. Results describe challenging weather-related aviation decisions and the information and cues used to support these decisions. Further, these results demonstrate the role of expertise in business aviation decision-making in weather flying, and how weather information is acquired and assessed for reliability. The challenging weather scenarios and novice errors identified in the results provide the basis for experimental scenarios and dependent measures to be used in future flight simulation evaluations of candidate aviation weather information systems. Finally, we analyzed these preliminary results to recommend design and training interventions to improve business aviation decision-making with weather information. The primary objective of this report is to present these preliminary findings and to document the extended CTA methodology used to elicit and represent expert business aviator decision-making with weather information. These preliminary findings will be augmented with results from additional subjects using this methodology. A summary of the complete results, absent the detailed treatment of methodology provided in this report, will be documented in a separate publication.

Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

International Nuclear Information System (INIS)

Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

2007-01-01

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

Cointegrating MiDaS Regressions and a MiDaS Test

OpenAIRE

J. Isaac Miller

2011-01-01

This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of the error term both serially and with the regressors, I find that nonlinear least squares consistent...

The development of an expert system for finding fragility curves of building structural systems in the preliminary design stage

International Nuclear Information System (INIS)

Yee, L.Y.; Okrent, D.

1987-01-01

This research is a starting point for the development of an expert system for determining seismic fragility curves of structural systems in a nuclear power plant or conventional building at the preliminary design stage. The resulting system assists an engineer with moderate engineering background and limited reliability knowledge to analyze the failure functions of building structures. It simulates the performance of an expert in identifying the potential failure modes and their variabilities for a structure of interest. On reviewing the methodology of seismic fragility evaluation for existing building structures in the nuclear power plant industry, one finds that the investigation process starts with the identification of critical components or substructures, whose failures result in the functional failure of safety related equipment or the failure of structural integrity itself, and follows with complicated numerical analyses to estimate the capacity functions associated with the limit states of these components or substructures

International consensus on preliminary definitions of improvement in adult and juvenile myositis.

Science.gov (United States)

Rider, Lisa G; Giannini, Edward H; Brunner, Hermine I; Ruperto, Nicola; James-Newton, Laura; Reed, Ann M; Lachenbruch, Peter A; Miller, Frederick W

2004-07-01

To use a core set of outcome measures to develop preliminary definitions of improvement for adult and juvenile myositis as composite end points for therapeutic trials. Twenty-nine experts in the assessment of myositis achieved consensus on 102 adult and 102 juvenile paper patient profiles as clinically improved or not improved. Two hundred twenty-seven candidate definitions of improvement were developed using the experts' consensus ratings as a gold standard and their judgment of clinically meaningful change in the core set of measures. Seventeen additional candidate definitions of improvement were developed from classification and regression tree analysis, a data-mining decision tree tool analysis. Six candidate definitions specifying percentage change or raw change in the core set of measures were developed using logistic regression analysis. Adult and pediatric working groups ranked the 13 top-performing candidate definitions for face validity, clinical sensibility, and ease of use, in which the sensitivity and specificity were >/=75% in adult, pediatric, and combined data sets. Nominal group technique was used to facilitate consensus formation. The definition of improvement (common to the adult and pediatric working groups) that ranked highest was 3 of any 6 of the core set measures improved by >/=20%, with no more than 2 worse by >/=25% (which could not include manual muscle testing to assess strength). Five and 4 additional preliminary definitions of improvement for adult and juvenile myositis, respectively, were also developed, with several definitions common to both groups. Participants also agreed to prospectively test 6 logistic regression definitions of improvement in clinical trials. Consensus preliminary definitions of improvement were developed for adult and juvenile myositis, and these incorporate clinically meaningful change in all myositis core set measures in a composite end point. These definitions require prospective validation, but they are now

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)

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

Directory of Open Access Journals (Sweden)

Guoqi Qian

2016-01-01

Full Text Available Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.

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...

Image superresolution using support vector regression.

Science.gov (United States)

Ni, Karl S; Nguyen, Truong Q

2007-06-01

A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.

Effects of climate change on Pacific Northwest water-related resources: Summary of preliminary findings

Energy Technology Data Exchange (ETDEWEB)

Scott, M.J.; Sands, R.D.; Vail, L.W.; Chatters, J.C.; Neitzel, D.A.; Shankle, S.A.

1993-12-01

The Pacific Northwest Case Study is a multi-agency analysis of atmospheric/climatic change impacts on the Pacific Northwest (which includes Washington, Oregon, Idaho, and portions of the Columbia River Basin in Western Montana). The purpose of the case study, which began in fiscal year 1991, was to develop and test analytical tools, as well as to develop an assessment of the effects of climate change on climate-sensitive natural resources of the Pacific Northwest and economic sectors dependent on them. The overall study, jointly funded by the US Department of Energy (DOE) and the US Environmental Protection Agency, was a broad-based, reconnaissance-level study to identify potential climate impacts on agriculture, coastal resources, forest resources, and irrigation in the Pacific Northwest. DOE participated in the reconnaissance study, with responsibility for hydroelectric and water supply issues. While this report briefly discusses a broader array of water issues, attention is mainly focused on three aspects of the water study: (1) the effects of the region`s higher temperatures on the demand for electric power (which in turn puts additional demand on hydroelectric resources of the region); (2) the effects of higher temperatures and changes, both in precipitation amounts and seasonality, on river flows and hydroelectric supply; and (3) the effect of higher temperatures and changed precipitation amounts and seasonality on salmonid resources -- particularly the rearing conditions in tributaries of the Columbia River Basin. Because the meaning of regional climate forecasts is still quite uncertain, most of the preliminary findings are based on sensitivity analyses and historical analog climate scenarios.

Functional magnetic resonance imaging (fMRI of attention processes in presumed obligate carriers of schizophrenia: preliminary findings

Directory of Open Access Journals (Sweden)

Morris Robin G

2008-10-01

Full Text Available Abstract Background Presumed obligate carriers (POCs are the first-degree relatives of people with schizophrenia who, although do not exhibit the disorder, are in direct lineage of it. Thus, this subpopulation of first-degree relatives could provide very important information with regard to the investigation of endophenotypes for schizophrenia that could clarify the often contradictory findings in schizophrenia high-risk populations. To date, despite the extant literature on schizophrenia endophenotypes, we are only aware of one other study that examined the neural mechanisms that underlie cognitive abnormalities in this group. The aim of this study was to investigate whether a more homogeneous group of relatives, such as POCs, have neural abnormalities that may be related to schizophrenia. Methods We used functional magnetic resonance imaging (fMRI to collect blood oxygenated level dependent (BOLD response data in six POCs and eight unrelated healthy controls while performing under conditions of sustained, selective and divided attention. Results The POCs indicated alterations in a widely distributed network of regions involved in attention processes, such as the prefrontal and temporal (including the parahippocampal gyrus cortices, in addition to the anterior cingulate gyrus. More specifically, a general reduction in BOLD response was found in these areas compared to the healthy participants during attention processes. Conclusion These preliminary findings of decreased activity in POCs indicate that this more homogeneous population of unaffected relatives share similar neural abnormalities with people with schizophrenia, suggesting that reduced BOLD activity in the attention network may be an intermediate marker for schizophrenia.

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...

Preliminary findings of an adapted evidence-based woman-focused HIV intervention on condom use and negotiation among at-risk women in Pretoria, South Africa.

Science.gov (United States)

Wechsberg, Wendee M; Luseno, Winnie K; Kline, Tracy L; Browne, Felicia A; Zule, William A

2010-01-01

This article presents the results of a randomized trial in South Africa of an adapted evidence-based Woman-Focused intervention on condom use with primary sex partners. The preliminary findings show that regardless of HIV status, condom negotiation was significantly associated with condom use at the 3- and 6-month follow-ups. By intervention group, significant intervention effects were found at 6-month follow-up for HIV-positive and HIV-unknown status women in the Woman-Focused intervention who were more likely than women in the Standard intervention to report condom use with a primary male partner. Among HIV-positive women, those in the Woman-Focused group and those with greater sexual control were more likely to report condom use at the 6-month follow-up. The findings indicate that gender-based interventions for women may result in increased condom negotiation skills.

The crux of the method: assumptions in ordinary least squares and logistic regression.

Science.gov (United States)

Long, Rebecca G

2008-10-01

Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.

Licensing support system preliminary needs analysis: Volume 1

International Nuclear Information System (INIS)

1989-01-01

This Preliminary Needs Analysis, together with the Preliminary Data Scope Analysis (next in this series of reports), is a first effort under the LSS Design and Implementation Contract toward developing a sound requirements foundation for subsequent design work. Further refinements must be made before requirements can be specified in sufficient detail to provide a basis for suitably specific system specifications. This preliminary analysis of the LSS requirements has been divided into a ''needs'' and a ''data scope'' portion only for project management and scheduling reasons. The Preliminary Data Scope Analysis will address all issues concerning the content and size of the LSS data base; providing the requirements basis for data acquisition, cataloging and storage sizing specifications. This report addresses all other requirements for the LSS. The LSS consists of both computer subsystems and non-computer archives. This study addresses only the computer subsystems, focusing on the Access Subsystems. After providing background on previous LSS-related work, this report summarizes the findings from previous examinations of needs and describes a number of other requirements that have an impact on the LSS. The results of interviews conducted for this report are then described and analyzed. The final section of the report brings all of the key findings together and describes how these needs analyses will continue to be refined and utilized in on-going design activities. 14 refs., 2 figs., 1 tab

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

Science.gov (United States)

Meaney, Christopher; Moineddin, Rahim

2014-01-24

In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

Science.gov (United States)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

Can typical US home visits affect infant attachment? Preliminary findings from a randomized trial of Healthy Families Durham.

Science.gov (United States)

Berlin, Lisa J; Martoccio, Tiffany L; Appleyard Carmody, Karen; Goodman, W Benjamin; O'Donnell, Karen; Williams, Janis; Murphy, Robert A; Dodge, Kenneth A

2017-12-01

US government-funded early home visiting services are expanding significantly. The most widely implemented home visiting models target at-risk new mothers and their infants. Such home visiting programs typically aim to support infant-parent relationships; yet, such programs' effects on infant attachment quality per se are as yet untested. Given these programs' aims, and the crucial role of early attachments in human development, it is important to understand attachment processes in home visited families. The current, preliminary study examined 94 high-risk mother-infant dyads participating in a randomized evaluation of the Healthy Families Durham (HFD) home visiting program. We tested (a) infant attachment security and disorganization as predictors of toddler behavior problems and (b) program effects on attachment security and disorganization. We found that (a) infant attachment disorganization (but not security) predicted toddler behavior problems and (b) participation in HFD did not significantly affect infant attachment security or disorganization. Findings are discussed in terms of the potential for attachment-specific interventions to enhance the typical array of home visiting services.

Prenatal diagnosis of Caudal Regression Syndrome : a case report

Directory of Open Access Journals (Sweden)

Celikaslan Nurgul

2001-12-01

Full Text Available Abstract Background Caudal regression is a rare syndrome which has a spectrum of congenital malformations ranging from simple anal atresia to absence of sacral, lumbar and possibly lower thoracic vertebrae, to the most severe form which is known as sirenomelia. Maternal diabetes, genetic predisposition and vascular hypoperfusion have been suggested as possible causative factors. Case presentation We report a case of caudal regression syndrome diagnosed in utero at 22 weeks' of gestation. Prenatal ultrasound examination revealed a sudden interruption of the spine and "frog-like" position of lower limbs. Termination of pregnancy and autopsy findings confirmed the diagnosis. Conclusion Prenatal ultrasonographic diagnosis of caudal regression syndrome is possible at 22 weeks' of gestation by ultrasound examination.

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...

Gaussian process regression for geometry optimization

Science.gov (United States)

Denzel, Alexander; Kästner, Johannes

2018-03-01

We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.

Regression Analysis: Instructional Resource for Cost/Managerial Accounting

Science.gov (United States)

Stout, David E.

2015-01-01

This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…

Multivariate Regression of Liver on Intestine of Mice: A ...

African Journals Online (AJOL)

Multivariate Regression of Liver on Intestine of Mice: A Chemotherapeutic Evaluation of Plant ... Using an analysis of covariance model, the effects ... The findings revealed, with the aid of likelihood-ratio statistic, a marked improvement in

Comparing shame in clinical and nonclinical populations: Preliminary findings.

Science.gov (United States)

Dyer, Kevin F W; Dorahy, Martin J; Corry, Mary; Black, Rebecca; Matheson, Laura; Coles, Holly; Curran, David; Seager, Lenaire; Middleton, Warwick

2017-03-01

To conduct a preliminary study comparing different trauma and clinical populations on types of shame coping style and levels of state shame and guilt. A mixed independent groups/correlational design was employed. Participants were recruited by convenience sampling of 3 clinical populations-complex trauma (n = 65), dissociative identity disorder (DID; n = 20), and general mental health (n = 41)-and a control group of healthy volunteers (n = 125). All participants were given (a) the Compass of Shame Scale, which measures the four common shame coping behaviors/styles of "withdrawal," "attack self," "attack other," and "avoidance," and (b) the State Shame and Guilt Scale, which assesses state shame, guilt, and pride. The DID group exhibited significantly higher levels of "attack self," "withdrawal," and "avoidance" relative to the other groups. The complex trauma and general mental health groups did not differ on any shame variable. All three clinical groups had significantly greater levels of the "withdrawal" coping style and significantly impaired shame/guilt/pride relative to the healthy volunteers. "Attack self" emerged as a significant predictor of increased state shame in the complex trauma, general mental health, and healthy volunteer groups, whereas "withdrawal" was the sole predictor of state shame in the DID group. DID emerged as having a different profile of shame processes compared to the other clinical groups, whereas the complex trauma and general mental health groups had comparable shame levels and variable relationships. These differential profiles of shame coping and state shame are discussed with reference to assessment and treatment. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Influence of age and gender in response to {gamma}-radiation in Portuguese individuals using chromosomal aberration assay - Preliminary findings

Energy Technology Data Exchange (ETDEWEB)

Martins, V.; Antunes, A.C. [Instituto Tecnologico e Nuclear, Unidade de Proteccao e Seguranca Radiologica, Dosimetry and Radiobiology Group, E.N. 10, Apartado 21, 2686-953 Sacavem (Portugal); Cardoso, J.; Santos, L. [Instituto Tecnologico e Nuclear, Unidade de Proteccao e Seguranca Radiologica, Metrology Laboratory of Ionizing Radiation, E.N. 10, Apartado 21, 2686-953 Sacavem (Portugal); Gil, O. Monteiro, E-mail: octavia.gil@itn.pt [Instituto Tecnologico e Nuclear, Unidade de Proteccao e Seguranca Radiologica, Dosimetry and Radiobiology Group, E.N. 10, Apartado 21, 2686-953 Sacavem (Portugal)

2011-09-15

Cytogenetic indicators are widely used in radiobiology to evaluate effects of ionizing radiation since dicentric chromosomes (Dic) are almost exclusively induced by ionizing radiation, and spontaneous frequency of Dic is very low in the healthy general population (about one Dic per 1000 cells). A particular interest of biodosimetry has been not only to obtain absorbed dose estimates using adequate calibration curves, under the assumption that all individuals respond equally to radiation-induced chromosome aberrations, but also to find a way to demonstrate inter-individual radiosensitivity and a possible correlation with age and gender. Thus, the objective of this preliminary work was the evaluation of the influence of age and gender on the outcome of cytogenetic biomarkers after {gamma}-irradiation. Samples of peripheral blood lymphocytes from six healthy, non-smoker, donors from both genders (three men and three women), in the range of 20 to 49 years, were irradiated with doses from 0 Gy to 3 Gy air kerma, using a {sup 60}Co gamma rays source with a dose rate from 170-180 mGy/min. A clear dose-dependent increase in terms of aberrant cells excluding gaps (ACEG) and Dic was observed for all donors. Our preliminary results suggest, in the higher dose level evaluated (3 Gy), a larger intervariability among individuals for Dic, with females apparently more sensitive than males (P<0.05). Considering the different age groups, male donors showed a decrease, with age, for Dic and ACEG at the higher dose and also, for the background level, in case of ACEG. Future work will consider the study of more individuals, from both genders and different ages, in order to verify if this tendency persists and to enable the implementation of a dose-response calibration curve at Instituto Tecnologico e Nuclear for the Portuguese population, to quantify the biological dose in case of a radiological accident or emergency.

Influence of age and gender in response to γ-radiation in Portuguese individuals using chromosomal aberration assay - Preliminary findings

International Nuclear Information System (INIS)

Martins, V.; Antunes, A.C.; Cardoso, J.; Santos, L.; Gil, O. Monteiro

2011-01-01

Cytogenetic indicators are widely used in radiobiology to evaluate effects of ionizing radiation since dicentric chromosomes (Dic) are almost exclusively induced by ionizing radiation, and spontaneous frequency of Dic is very low in the healthy general population (about one Dic per 1000 cells). A particular interest of biodosimetry has been not only to obtain absorbed dose estimates using adequate calibration curves, under the assumption that all individuals respond equally to radiation-induced chromosome aberrations, but also to find a way to demonstrate inter-individual radiosensitivity and a possible correlation with age and gender. Thus, the objective of this preliminary work was the evaluation of the influence of age and gender on the outcome of cytogenetic biomarkers after γ-irradiation. Samples of peripheral blood lymphocytes from six healthy, non-smoker, donors from both genders (three men and three women), in the range of 20 to 49 years, were irradiated with doses from 0 Gy to 3 Gy air kerma, using a 60 Co gamma rays source with a dose rate from 170-180 mGy/min. A clear dose-dependent increase in terms of aberrant cells excluding gaps (ACEG) and Dic was observed for all donors. Our preliminary results suggest, in the higher dose level evaluated (3 Gy), a larger intervariability among individuals for Dic, with females apparently more sensitive than males (P<0.05). Considering the different age groups, male donors showed a decrease, with age, for Dic and ACEG at the higher dose and also, for the background level, in case of ACEG. Future work will consider the study of more individuals, from both genders and different ages, in order to verify if this tendency persists and to enable the implementation of a dose-response calibration curve at Instituto Tecnologico e Nuclear for the Portuguese population, to quantify the biological dose in case of a radiological accident or emergency.

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.

Is there still a place for the concept of 'therapeutic regression' in psychoanalysis?

Science.gov (United States)

Spurling, Laurence S

2008-06-01

The author uses his own failure to find a place for the idea of therapeutic regression in his clinical thinking or practice as the basis for an investigation into its meaning and usefulness. He makes a distinction between three ways the term 'regression' is used in psychoanalytic discourse: as a way of evoking a primitive level of experience; as a reminder in some clinical situations of the value of non-intervention on the part of the analyst; and as a description of a phase of an analytic treatment with some patients where the analyst needs to put aside normal analytic technique in order to foster a regression in the patient. It is this third meaning, which the author terms "therapeutic regression" that this paper examines, principally by means of an extended discussion of two clinical examples of a patient making a so-called therapeutic regression, one given by Winnicott and the other by Masud Khan. The author argues that in these examples the introduction of the concept of therapeutic regression obscures rather than clarifies the clinical process. He concludes that, as a substantial clinical concept, the idea of therapeutic regression has outlived its usefulness. However he also notes that many psychoanalytic writers continue to find a use for the more generic concept of regression, and that the very engagement with the more particular idea of therapeutic regression has value in provoking questions as to what is truly therapeutic in psychoanalytic treatment.

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

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.

Lung function in adults following in utero and childhood exposure to arsenic in drinking water: preliminary findings.

Science.gov (United States)

Dauphiné, David C; Ferreccio, Catterina; Guntur, Sandeep; Yuan, Yan; Hammond, S Katharine; Balmes, John; Smith, Allan H; Steinmaus, Craig

2011-08-01

Evidence suggests that arsenic in drinking water causes non-malignant lung disease, but nearly all data concern exposed adults. The desert city of Antofagasta (population 257,976) in northern Chile had high concentrations of arsenic in drinking water (>800 μg/l) from 1958 until 1970, when a new treatment plant was installed. This scenario, with its large population, distinct period of high exposure, and accurate data on past exposure, is virtually unprecedented in environmental epidemiology. We conducted a pilot study on early-life arsenic exposure and long-term lung function. We present these preliminary findings because of the magnitude of the effects observed. We recruited a convenience sample consisting primarily of nursing school employees in Antofagasta and Arica, a city with low drinking water arsenic. Lung function and respiratory symptoms in 32 adults exposed to >800 μg/l arsenic before age 10 were compared to 65 adults without high early-life exposure. Early-life arsenic exposure was associated with 11.5% lower forced expiratory volume in 1 s (FEV(1)) (P = 0.04), 12.2% lower forced vital capacity (FVC) (P = 0.04), and increased breathlessness (prevalence odds ratio = 5.94, 95% confidence interval 1.36-26.0). Exposure-response relationships between early-life arsenic concentration and adult FEV(1) and FVC were also identified (P trend = 0.03). Early-life exposure to arsenic in drinking water may have irreversible respiratory effects of a magnitude similar to smoking throughout adulthood. Given the small study size and non-random recruitment methods, further research is needed to confirm these findings.

Forecasting urban water demand: A meta-regression analysis.

Science.gov (United States)

Sebri, Maamar

2016-12-01

Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

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...

Considering a non-polynomial basis for local kernel regression problem

Science.gov (United States)

Silalahi, Divo Dharma; Midi, Habshah

2017-01-01

A common used as solution for local kernel nonparametric regression problem is given using polynomial regression. In this study, we demonstrated the estimator and properties using maximum likelihood estimator for a non-polynomial basis such B-spline to replacing the polynomial basis. This estimator allows for flexibility in the selection of a bandwidth and a knot. The best estimator was selected by finding an optimal bandwidth and knot through minimizing the famous generalized validation function.

Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series.

Science.gov (United States)

Hahn, Andrew D; Rowe, Daniel B

2012-02-01

As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression. Our findings suggest that including physiologic regressors provides some benefit in terms of reduction in phase noise power, but it is small compared to the benefit of dynamic field corrections and use of estimated motion parameters as nuisance regressors. Additionally, we show that the use of all three techniques reduces phase variance substantially, removes undesirable spatial phase correlations and improves detection of the functional response in magnitude and phase. Copyright © 2011 Elsevier Inc. All rights reserved.

Articulation Rate and Vowel Space Characteristics of Young Males with Fragile X Syndrome: Preliminary Acoustic Findings

Science.gov (United States)

Zajac, David J.; Roberts, Joanne E.; Hennon, Elizabeth A.; Harris, Adrianne A.; Barnes, Elizabeth F.; Misenheimer, Jan

2006-01-01

Purpose: Increased speaking rate is a commonly reported perceptual characteristic among males with fragile X syndrome (FXS). The objective of this preliminary study was to determine articulation rate--one component of perceived speaking rate--and vowel space characteristics of young males with FXS. Method: Young males with FXS (n = 38), …

Influence of study satisfaction on academic procrastination in psychology students: a preliminary study

Directory of Open Access Journals (Sweden)

Sergio Alexis Dominguez-Lara

2017-06-01

Full Text Available The aim of this predictive study was to analyze the degree of influence of study satisfaction (SS on academic procrastination (AP. One hundred forty- eight (148 psychology students (111 women between 18 and 32 years old (M = 22.41 were evaluated using the Brief Scale of Study Satisfaction and the Academic Procrastination Scale. After preliminary analyses focused on the scores reliability (α > 0.70 and correlations between dimensions, a regression analysis was performed to determine how much of the variability in the AP dimensions’ scores is explained by the variations in the SS. For that purpose, a method that uses bivariate correlations corrected for attenuation and provides confidence intervals under a bootstrap approach of the associated statistics was applied. All analyses were assessed from an effect size approach. The results indicate that the influence of SS on AP was not significant. These findings provide new ways to implement studies in order to understand the procrastinating behavior in the university setting.

High-resolution CT findings in infants with bronchopulmonary dysplasia: preliminary report

International Nuclear Information System (INIS)

Chung, Yoon Ho; Lee, Young Seok; Kim, Ji Hye; Han, Heon; Chung, Hyo Sun; Cha, Yoo Mi; Kim, Young Chae; Kim, Sang Hee

1996-01-01

To evaluate high resolution CT(HRCT) findings in infants with bronchopulmonary dysplasia(BPD). In 13 infants(age range, 1-12 months;11 premature babies, two full-term babies; birth weight, 0.97-3.88kg;mean 2,03kg) with clinico-radiologically suggested BPD, HRCT findings of the lung were reviewed retrospectively. Spiral CT using ultra high bone algorithm, 1mm collimation with 5-8mm interval, and 0.7sec scan time was performed without regard to breathing-control of infants. Three radiologists each analysed the HRCT findings twice. HRCT findings of BPD were as follows:parenchymal bands(n=13), interlobular septal thickenings (n=12), multifocal hyperaeration involving lobar or segmental distribution(n=7), and involving lobular distribution or small cyst-like lesion(n=4), centrilobular nodules(n=7), consolidation and/or atelectasis(n=7), and bronchovascular bundle thickening(n=6). Parenchymal bands, interlobular septal thickenings, and multifocal hyperaerations were the major findings in cases of bronchopulmonary dysplasia whereas, centrilobular nodules, consolidation and/or atelectasis, and bronchovascular bundle thickenings were the minor findings. These findings may be used as basic data in the evaluation of BPD in future studies

Tax Evasion, Information Reporting, and the Regressive Bias Prediction

DEFF Research Database (Denmark)

Boserup, Simon Halphen; Pinje, Jori Veng

2013-01-01

evasion and audit probabilities once we account for information reporting in the tax compliance game. When conditioning on information reporting, we find that both reduced-form evidence and simulations exhibit the predicted regressive bias. However, in the overall economy, this bias is negated by the tax......Models of rational tax evasion and optimal enforcement invariably predict a regressive bias in the effective tax system, which reduces redistribution in the economy. Using Danish administrative data, we show that a calibrated structural model of this type replicates moments and correlations of tax...

Utilizing the ECHO Model in the Veterans Health Affairs System: Guidelines for Setup, Operations and Preliminary Findings

Directory of Open Access Journals (Sweden)

Herschel Knapp

2015-06-01

Full Text Available Background: In 2011, the Veterans Health Administration (VHA consulted with the Project ECHO (Extension for Community Healthcare Outcomes team at the University of New Mexico, Albuquerque, to reproduce their successful model within the VHA. Methods: The VHA launched SCAN-ECHO (Specialty Care Access Network-Extension for Community Healthcare Outcomes, a multisite videoconferencing system to conduct live clinical consultations between specialists at a VHA Medical Center (hospital and primary care providers stationed at satellite VHA CBOCs (Community-Based Outpatient Clinic. Results: Analysis of the first three years rendered a mean attendee satisfaction of 89.53% and a consultation satisfaction score of 88.10%. About half of the SCAN-ECHO consultations resulted in patients receiving their treatment from their local primary care providers; the remaining half were referred to the VHA Medical Center when the treatment involved equipment or services not available at the CBOCs (e.g., MRI, surgery. Conclusion: This paper details the setup, operation logistics and preliminary findings, suggesting that SCAN-ECHO is a viable model for providing quality specialty clinical consultation service, prompter access to care, reduced commutes and continuing education. Additionally, the use of a secured Internet-based videoconferencing system that supports connectivity to multiple (mobile devices could expand the utilization of this service.

Prognostic Indicators of Gingival Recession in Nigeria: Preliminary Findings

Directory of Open Access Journals (Sweden)

Michael Adedigba

2010-06-01

Full Text Available AIM: Literature is replete with studies on gingival recession, the apical shift of the gingival margin from the cemento-enamel junction. Chronic periodontitis and frequent toothbrushing are among its aetiological factors. Many of these were however prevalence studies. The current study was therefore aimed at separating prognostic indicators from determinants of the number of recessions. METHOD: 650 consecutive adult patients visiting a Nigerian teaching hospital were examined using a checklist including plaque, calculus, Miller’s class of recession and other parameters.. A total of 408 recession sites were identified. RESULTS: The mean age of the patients with recession was 42.3 years; mean number of recession was 4.74 Incisors had the highest number of recessions (35.7%. While a factor such as age was related both to the number and prognosis of recession sites, abrasion and plaque were only related to prognosis. Again, some of the factors previously significantly related to prognosis on univariate analysis like calculus and smoking, lost their significance on regression analysis. CONCLUSION: The three strongest predictors of prognosis (Miller’s class of recession were age, plaque and abrasion. [TAF Prev Med Bull 2010; 9(3.000: 187-194

Smoking topography in Korean American and white men: preliminary findings.

Science.gov (United States)

Chung, Sangkeun; Kim, Sun S; Kini, Nisha; Fang, Hua J; Kalman, David; Ziedonis, Douglas M

2015-06-01

This is the first study of Korean Americans' smoking behavior using a topography device. Korean American men smoke at higher rates than the general U.S. Korean American and White men were compared based on standard tobacco assessment and smoking topography measures. They smoked their preferred brand of cigarettes ad libitum with a portable smoking topography device for 24 h. Compared to White men (N = 26), Korean American men (N = 27) were more likely to smoke low nicotine-yield cigarettes (p Whites. Controlling for the number of cigarettes smoked, Koreans smoked with higher average puff flows (p = 0.05), greater peak puff flows (p = 0.02), and shorter interpuff intervals (p Whites. Puff counts, puff volumes, and puff durations did not differ between the two groups. This study offers preliminary insight into unique smoking patterns among Korean American men who are likely to smoke low nicotine-yield cigarettes. We found that Korean American men compensated their lower number and low nicotine-yield cigarettes by smoking with greater puff flows and shorter interpuff intervals than White men, which may suggest exposures to similar amounts of nicotine and harmful tobacco toxins by both groups. Clinicians will need to consider in identifying and treating smokers in a mutually aggressive manner, irrespective of cigarette type and number of cigarette smoked per day.

Caudal regression with sirenomelia and dysplasia renofacialis (Potter's syndrome)

International Nuclear Information System (INIS)

Noeldge, G.; Billmann, P.; Boehm, N.; Freiburg Univ.

1982-01-01

A case of caudal regression in combination with sirenomelia and dysplasia renofacialis (Potter's syndrome) is reported. The formal pathogenesis of these malformations and clinical facts are shown and discussed. Findings of plain films, postmortal angiography and pathologic-anatomical changes are demonstrated. (orig.) [de

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…

Comparison of pre-operative dGEMRIC imaging with intra-operative findings in femoroacetabular impingement: preliminary findings

International Nuclear Information System (INIS)

Bittersohl, Bernd; Apprich, Sebastian; Siebenrock, Klaus A.; Mamisch, Tallal Charles; Hosalkar, Harish S.; Werlen, Stefan A.

2011-01-01

To study standard MRI and dGEMRIC in patients with symptomatic FAI undergoing surgical intervention and compare them with intra-operative findings to see if they were corroborative. Sixteen patients with symptomatic FAI that warranted surgical intervention were prospectively studied. All patients underwent plain radiographic series for FAI assessment followed by standard MRI and dGEMRIC. Subsequently, patients were surgically treated with safe dislocation and the joint was evaluated for any macroscopic signs of damaged cartilage. Data were statistically analyzed. A total of 224 zones in 16 patients were evaluated. One hundred and sixteen zones were intra-operatively rated as normal with mean T1 values of 510.1 ms ± 141.2 ms. Eighty zones had evidence of damage with mean T1 values of 453.1 ms ± 113.6 ms. The difference in these T1 values was significant (p = 0.003). Correlation between standard MRI and intra-operative findings was moderate (r = 0.535, p < 0.001). Intra-operative findings revealed more damage than standard MRI. On standard MRI, 68.6% zones were graded normal while 31.4% had evidence of damage. On intra-operative visualization, 56.4% zones were graded normal and 43.6% had evidence of damage. Correlation between dGEMRIC and intra-operative findings turned out to be weak (r = 0.114, p < 0.126). On T1 assessment 31.4% of zones were graded as normal and 68.6% as damaged. dGEMRIC was significantly different between normal and affected cartilage based on intra-operative assessment. The correlation for morphological findings was limited, underestimating defects. By combining morphological with biochemical assessment dGEMRIC may play some role in the future to prognosticate outcomes and facilitate surgical planning and intervention. (orig.)

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

Science.gov (United States)

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

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.

Time-adaptive quantile regression

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

2008-01-01

and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

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.

A novel attention training paradigm based on operant conditioning of eye gaze: Preliminary findings.

Science.gov (United States)

Price, Rebecca B; Greven, Inez M; Siegle, Greg J; Koster, Ernst H W; De Raedt, Rudi

2016-02-01

Inability to engage with positive stimuli is a widespread problem associated with negative mood states across many conditions, from low self-esteem to anhedonic depression. Though attention retraining procedures have shown promise as interventions in some clinical populations, novel procedures may be necessary to reliably attenuate chronic negative mood in refractory clinical populations (e.g., clinical depression) through, for example, more active, adaptive learning processes. In addition, a focus on individual difference variables predicting intervention outcome may improve the ability to provide such targeted interventions efficiently. To provide preliminary proof-of-principle, we tested a novel paradigm using operant conditioning to train eye gaze patterns toward happy faces. Thirty-two healthy undergraduates were randomized to receive operant conditioning of eye gaze toward happy faces (train-happy) or neutral faces (train-neutral). At the group level, the train-happy condition attenuated sad mood increases following a stressful task, in comparison to train-neutral. In individual differences analysis, greater physiological reactivity (pupil dilation) in response to happy faces (during an emotional face-search task at baseline) predicted decreased mood reactivity after stress. These Preliminary results suggest that operant conditioning of eye gaze toward happy faces buffers against stress-induced effects on mood, particularly in individuals who show sufficient baseline neural engagement with happy faces. Eye gaze patterns to emotional face arrays may have a causal relationship with mood reactivity. Personalized medicine research in depression may benefit from novel cognitive training paradigms that shape eye gaze patterns through feedback. Baseline neural function (pupil dilation) may be a key mechanism, aiding in iterative refinement of this approach. (c) 2016 APA, all rights reserved).

Preliminary findings radon daughter levels in structures constructed on reclaimed Florida phosphate land

International Nuclear Information System (INIS)

1975-09-01

Preliminary results are reported from a survey of the radon daughter levels in structures in Polk County, Florida, built on reclaimed phosphate tailings containing various amounts of 226 Ra. The structures surveyed consisted primarily of private dwellings although a few office buildings were also surveyed. Track-etch films and TLD air samplers were used to measure the levels of radon daughters within the structures and in structures built on non-phosphate land. Radiation levels were converted to WL units (the working level (WL) unit is defined as the potential α energy from the short-lived daughters of Rn which will produce 1.3 x 10 5 MeV in one liter of air). The highest observed level in any structure was 0.2 WL. Possible health hazards to long-time occupants are discussed

Benefit finding for Chinese family caregivers of community-dwelling stroke survivors: A cross-sectional study.

Science.gov (United States)

Mei, Yongxia; Wilson, Susan; Lin, Beilei; Li, Yingshuang; Zhang, Zhenxiang

2018-04-01

To identify whether benefit finding is a mediator or moderator in the relationship between caregiver burden and psychological well-being (anxiety and depression) in Chinese family caregivers of community-dwelling stroke survivors. Family caregivers not only bear a heavy burden, a high level of anxiety and depression, but also experience benefit finding (positive effects result from stressful events). However, the relationships among benefit finding, caregiver burden and psychological well-being in Chinese family caregivers are not well known. This study was a cross-sectional correlational design. Caregivers (n = 145) of stroke survivors were recruited from two communities in Zhengzhou, China. Data were collected by face-to-face interviews with structured questionnaires, examining caregiver burden, benefit finding and psychological well-being of caregivers. A hierarchical regression analysis explored whether caregiver burden and benefit finding were associated with anxiety and depression of caregivers. The moderator role of benefit finding was examined by testing the significance of the interaction between caregiver burden and benefit finding. A mediational model was used to test benefit finding as a mediator between caregiver burden and psychological well-being of caregivers using process in spss 21.0. Caregiver burden and benefit finding were significantly associated with both anxiety and depression of caregivers. Benefit finding did not portray a moderating role, but portrayed the mediator role in the relationship between caregiver burden, anxiety and depression in caregivers. This study provides the preliminary evidence to nurses that intervention focus on benefit finding may help improve the psychological well-being of caregivers. This study offers nurses rational for assessing caregiver's negative emotions and benefit finding. By targeting benefit finding, the nurse may guide caregivers in benefit identification and implement interventions to reduce anxiety

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

Science.gov (United States)

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

Science.gov (United States)

Ernst, Anja F; Albers, Casper J

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

Directory of Open Access Journals (Sweden)

Suduan Chen

2014-01-01

Full Text Available As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Testing the Perturbation Sensitivity of Abortion-Crime Regressions

Directory of Open Access Journals (Sweden)

Michał Brzeziński

2012-06-01

Full Text Available The hypothesis that the legalisation of abortion contributed significantly to the reduction of crime in the United States in 1990s is one of the most prominent ideas from the recent “economics-made-fun” movement sparked by the book Freakonomics. This paper expands on the existing literature about the computational stability of abortion-crime regressions by testing the sensitivity of coefficients’ estimates to small amounts of data perturbation. In contrast to previous studies, we use a new data set on crime correlates for each of the US states, the original model specifica-tion and estimation methodology, and an improved data perturbation algorithm. We find that the coefficients’ estimates in abortion-crime regressions are not computationally stable and, therefore, are unreliable.

Smoking Topography in Korean American and White Men: Preliminary Findings

Science.gov (United States)

Chung, Sangkeun; Kim, Sun S; Kini, Nisha; Fang, Hua J; Kalman, David; Ziedonis, Douglas M.

2013-01-01

Introduction This is the first study of Korean Americans’ smoking behavior using a topography device. Korean American men smoke at higher rates than the general U.S. population. Methods Korean American and White men were compared based on standard tobacco assessment and smoking topography measures. They smoked their preferred brand of cigarettes ad libitum with a portable smoking topography device for 24 hours. Results Compared to White men (N = 26), Korean American men (N = 27) were more likely to smoke low nicotine-yield cigarettes (p < 0.001) and have lower Fagerstrom nicotine dependence scores (p = 0.04). Koreans smoked fewer cigarettes with the device (p = 0.01) than Whites. Controlling for the number of cigarettes smoked, Koreans smoked with higher average puff flows (p = 0.05), greater peak puff flows (p = 0.02), and shorter interpuff intervals (p < 0.001) than Whites. Puff counts, puff volumes, and puff durations did not differ between the two groups. Conclusions This study offers preliminary insight into unique smoking patterns among Korean American men who are likely to smoke low nicotine-yield cigarettes. We found that Korean American men compensated their lower number and low nicotine-yield cigarettes by smoking more frequently with greater puff flows than White men, which may suggest exposures to similar amounts of nicotine and harmful tobacco toxins by both groups. Clinicians will need to consider in identifying and treating smokers in a mutually aggressive manner, irrespective of cigarette type and number of cigarette smoked per day. PMID:24068611

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

On a Robust MaxEnt Process Regression Model with Sample-Selection

Directory of Open Access Journals (Sweden)

Hea-Jung Kim

2018-04-01

Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

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-

A preliminary assessment of the potential risks from electrical ...

African Journals Online (AJOL)

... Grey-crowned Crane Balearica regulorum, Lesser Flamingo Phoeniconaias minor, White-backed Vulture Gyps africanus, Rüppell's Vulture Gyps rueppellii, Martial Eagle Polemaetus bellicosus, White Stork Ciconia ciconia, Secretarybird Sagittarius serpentarius, and various sit-and-wait raptors. These preliminary findings ...

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…

Everyday episodic memory in amnestic mild cognitive impairment: a preliminary investigation.

Science.gov (United States)

Irish, Muireann; Lawlor, Brian A; Coen, Robert F; O'Mara, Shane M

2011-08-04

Decline in episodic memory is one of the hallmark features of Alzheimer's disease (AD) and is also a defining feature of amnestic Mild Cognitive Impairment (MCI), which is posited as a potential prodrome of AD. While deficits in episodic memory are well documented in MCI, the nature of this impairment remains relatively under-researched, particularly for those domains with direct relevance and meaning for the patient's daily life. In order to fully explore the impact of disruption to the episodic memory system on everyday memory in MCI, we examined participants' episodic memory capacity using a battery of experimental tasks with real-world relevance. We investigated episodic acquisition and delayed recall (story-memory), associative memory (face-name pairings), spatial memory (route learning and recall), and memory for everyday mundane events in 16 amnestic MCI and 18 control participants. Furthermore, we followed MCI participants longitudinally to gain preliminary evidence regarding the possible predictive efficacy of these real-world episodic memory tasks for subsequent conversion to AD. The most discriminating tests at baseline were measures of acquisition, delayed recall, and associative memory, followed by everyday memory, and spatial memory tasks, with MCI patients scoring significantly lower than controls. At follow-up (mean time elapsed: 22.4 months), 6 MCI cases had progressed to clinically probable AD. Exploratory logistic regression analyses revealed that delayed associative memory performance at baseline was a potential predictor of subsequent conversion to AD. As a preliminary study, our findings suggest that simple associative memory paradigms with real-world relevance represent an important line of enquiry in future longitudinal studies charting MCI progression over time.

Everyday episodic memory in amnestic mild cognitive impairment: a preliminary investigation

Directory of Open Access Journals (Sweden)

Lawlor Brian A

2011-08-01

Full Text Available Abstract Background Decline in episodic memory is one of the hallmark features of Alzheimer's disease (AD and is also a defining feature of amnestic Mild Cognitive Impairment (MCI, which is posited as a potential prodrome of AD. While deficits in episodic memory are well documented in MCI, the nature of this impairment remains relatively under-researched, particularly for those domains with direct relevance and meaning for the patient's daily life. In order to fully explore the impact of disruption to the episodic memory system on everyday memory in MCI, we examined participants' episodic memory capacity using a battery of experimental tasks with real-world relevance. We investigated episodic acquisition and delayed recall (story-memory, associative memory (face-name pairings, spatial memory (route learning and recall, and memory for everyday mundane events in 16 amnestic MCI and 18 control participants. Furthermore, we followed MCI participants longitudinally to gain preliminary evidence regarding the possible predictive efficacy of these real-world episodic memory tasks for subsequent conversion to AD. Results The most discriminating tests at baseline were measures of acquisition, delayed recall, and associative memory, followed by everyday memory, and spatial memory tasks, with MCI patients scoring significantly lower than controls. At follow-up (mean time elapsed: 22.4 months, 6 MCI cases had progressed to clinically probable AD. Exploratory logistic regression analyses revealed that delayed associative memory performance at baseline was a potential predictor of subsequent conversion to AD. Conclusions As a preliminary study, our findings suggest that simple associative memory paradigms with real-world relevance represent an important line of enquiry in future longitudinal studies charting MCI progression over time.

Everyday episodic memory in amnestic Mild Cognitive Impairment: a preliminary investigation

LENUS (Irish Health Repository)

Irish, Muireann

2011-08-04

Abstract Background Decline in episodic memory is one of the hallmark features of Alzheimer\\'s disease (AD) and is also a defining feature of amnestic Mild Cognitive Impairment (MCI), which is posited as a potential prodrome of AD. While deficits in episodic memory are well documented in MCI, the nature of this impairment remains relatively under-researched, particularly for those domains with direct relevance and meaning for the patient\\'s daily life. In order to fully explore the impact of disruption to the episodic memory system on everyday memory in MCI, we examined participants\\' episodic memory capacity using a battery of experimental tasks with real-world relevance. We investigated episodic acquisition and delayed recall (story-memory), associative memory (face-name pairings), spatial memory (route learning and recall), and memory for everyday mundane events in 16 amnestic MCI and 18 control participants. Furthermore, we followed MCI participants longitudinally to gain preliminary evidence regarding the possible predictive efficacy of these real-world episodic memory tasks for subsequent conversion to AD. Results The most discriminating tests at baseline were measures of acquisition, delayed recall, and associative memory, followed by everyday memory, and spatial memory tasks, with MCI patients scoring significantly lower than controls. At follow-up (mean time elapsed: 22.4 months), 6 MCI cases had progressed to clinically probable AD. Exploratory logistic regression analyses revealed that delayed associative memory performance at baseline was a potential predictor of subsequent conversion to AD. Conclusions As a preliminary study, our findings suggest that simple associative memory paradigms with real-world relevance represent an important line of enquiry in future longitudinal studies charting MCI progression over time.

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.

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

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

Science.gov (United States)

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Genetic Predictors of Fatigue in Prostate Cancer Patients Treated with Androgen Deprivation Therapy: Preliminary Findings

Science.gov (United States)

Jim, Heather S.L.; Park, Jong Y.; Permuth-Wey, Jennifer; Rincon, Maria A.; Phillips, Kristin M.; Small, Brent J.; Jacobsen, Paul B.

2012-01-01

Background Fatigue is a common and distressing side effect of androgen deprivation therapy (ADT) for prostate cancer. The goal of the current study was to examine the relationship between changes in fatigue following initiation of ADT and single nucleotide polymorphisms (SNPs) in three pro-inflammatory cytokine genes: interleukin-1 beta (IL1B), interleukin-6 (IL6), and tumor necrosis factor alpha (TNFA). Methods As part of a larger study, men with prostate cancer (n=53) were recruited prior to initiation of ADT. Fatigue was assessed at recruitment and six months after initiation of ADT. DNA was extracted from blood drawn at baseline. Results Patients with the IL6-174 (rs1800795) G/C or C/C genotype displayed greater increases in fatigue intrusiveness, frequency, and duration than the G/G genotype (p values≤0.05), although inclusion of age, race, and baseline depressive symptomatology in the model attenuated these relationships (p values≤0.09). Patients with the TNFA-308 (rs1800629) G/A genotype showed greater increases in fatigue severity than the G/G genotype (p=0.02). IL1B-511 (rs16944) genotype did not significantly predict changes in fatigue (p values>0.46). Patients with higher numbers of variants displayed greater increases in fatigue duration and interference (p values≤0.02) than patients with lower numbers of variants. Conclusions Prostate cancer patients treated with ADT who carry variant alleles of the IL6 and TNFA genes are susceptible to heightened fatigue. These preliminary data lend support for the role of genetic variation in the development of cancer-related fatigue secondary to ADT. Findings are relevant to attempts to develop personalized approaches to cancer treatment. PMID:22475653

DIABETES MELLITUS AND ITS ROLE IN CAUDAL REGRESSION SYNDROME

Directory of Open Access Journals (Sweden)

Sandeep

2016-03-01

Full Text Available BACKGROUND Caudal regression syndrome also called as sacral agenesis or hypoplasia of the sacrum is a congenital disorder in which there is abnormal development of the lower part of the vertebral column 1 due to which there is a plethora of abnormalities such as gross motor deficiencies and other genitor-urinary malformations which in deed depends on the extent of malformations that is seen. Caudal regression syndrome is rare, with an estimated incidence of 1:7500-100,000. The aim of the study is to find the frequency of manifestations and the manifestations itself. METHODS Fifty patients who were pregnant and were diagnosed with diabetes mellitus were identified and were referred to the Department of Medicine. RESULTS In the present study the frequency of manifestations of caudal regression syndrome is 8 in 100 diagnosed patients. CONCLUSION The malformations in the babies born to diabetic mothers are high in the population of costal Karnataka and Kerala.

Preliminary assessment on the competency of technical staff of Atomic Energy Licensing Board

International Nuclear Information System (INIS)

Marina Mishar; Redzuwan Yahya

2010-01-01

The main purpose of this study is to carry out a preliminary assessment on the competency level of technical staff of Atomic Energy Licensing Board (AELB), the nuclear regulatory body in Malaysia for effectively monitoring and supervising the activities of the first nuclear power plant in Malaysia. The study is conducted out on the whole group of AELB technical staff, comprising 81 personnel from the supporting and professional categories. Findings showed that AELB technical staff who have been in the workforce for more than ten years have the required competency level for legal and regulatory processes competencies, regulatory practices competencies and effectiveness in personal and inter-personal competencies. Regression analysis between competency and working experience (length of service) showed a weak positive correlation, which could be contributed by job not related to the competency parameters for major functions of a regulatory body in controlling nuclear activity of a nuclear power plant. Results obtained could assist AELB in manpower development once Malaysia makes the decision to embark on a nuclear power programme. (author)

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

Science.gov (United States)

Ernst, Anja F.

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

Directory of Open Access Journals (Sweden)

Anja F. Ernst

2017-05-01

Full Text Available Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

Understanding poisson regression.

Science.gov (United States)

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

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

Malawi faith communities responding to HIV/AIDS: preliminary ...

African Journals Online (AJOL)

This paper reports on the preliminary findings (year one) of a four-year intervention and participatory-action research (PAR) project in Malawi. Project goals are to enhance the response capacity and effectiveness of faith community (FC) leaders to the problem of HIV/AIDS. Ethnographic interviews with FC leaders were ...

Gratitude, abstinence, and alcohol use disorders: Report of a preliminary finding.

Science.gov (United States)

Krentzman, Amy R

2017-07-01

Gratitude is a central component of addiction recovery for many, yet it has received scant attention in addiction research. In a sample of 67 individuals entering abstinence-based alcohol-use-disorder treatment, this study employed gratitude and abstinence variables from sequential assessments (baseline, 6months, 12months) to model theorized causal relationships: gratitude would increase pre-post treatment and gratitude after treatment would predict greater percent days abstinent 6months later. Neither hypothesis was supported. This unexpected result led to the theory that gratitude for sobriety was the construct of interest; therefore, the association between gratitude and future abstinence would be positive among those already abstinent. Thus, post-treatment abstinence was tested as a moderator of the effect of gratitude on future abstinence: this effect was statistically significant. For those who were abstinent after treatment, the relationship between gratitude and future abstinence was positive; for those drinking most frequently after treatment, the relationship between gratitude and future abstinence was negative. In this preliminary study, dispositional tendency to affirm that there is much to be thankful for appeared to perpetuate the status quo-frequent drinkers with high gratitude were drinking frequently 6months later; abstinent individuals with high gratitude were abstinent 6months later. Gratitude exercises might be contraindicated for clients who are drinking frequently and have abstinence as their treatment goal. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Spontaneous regression of cerebral arteriovenous malformations: clinical and angiographic analysis with review of the literature

International Nuclear Information System (INIS)

Lee, S.K.; Vilela, P.; Willinsky, R.; TerBrugge, K.G.

2002-01-01

Spontaneous regression of cerebral arteriovenous malformation (AVM) is rare and poorly understood. We reviewed the clinical and angiographic findings in patients who had spontaneous regression of cerebral AVMs to determine whether common features were present. The clinical and angiographic findings of four cases from our series and 29 cases from the literature were retrospectively reviewed. The clinical and angiographic features analyzed were: age at diagnosis, initial presentation, venous drainage pattern, number of draining veins, location of the AVM, number of arterial feeders, clinical events during the interval period to thrombosis, and interval period to spontaneous thrombosis. Common clinical and angiographic features of spontaneous regression of cerebral AVMs are: intracranial hemorrhage as an initial presentation, small AVMs, and a single draining vein. Spontaneous regression of cerebral AVMs can not be predicted by clinical or angiographic features, therefore it should not be considered as an option in cerebral AVM management, despite its proven occurrence. (orig.)

Quantum algorithm for linear regression

Science.gov (United States)

Wang, Guoming

2017-07-01

We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.

Preliminary characterization of abandoned septic tank systems. Volume 1

International Nuclear Information System (INIS)

1995-12-01

This report documents the activities and findings of the Phase I Preliminary Characterization of Abandoned Septic Tank Systems. The purpose of the preliminary characterization activity was to investigate the Tiger Team abandoned septic systems (tanks and associated leachfields) for the purpose of identifying waste streams for closure at a later date. The work performed was not to fully characterize or remediate the sites. The abandoned systems potentially received wastes or effluent from buildings which could have discharged non-domestic, petroleum hydrocarbons, hazardous, radioactive and/or mixed wastes. A total of 20 sites were investigated for the preliminary characterization of identified abandoned septic systems. Of the 20 sites, 19 were located and characterized through samples collected from each tank(s) and, where applicable, associated leachfields. The abandoned septic tank systems are located in Areas 5, 12, 15, 25, and 26 on the Nevada Test Site

Report on the preliminary fact finding mission following the accident at the nuclear fuel processing facility in Tokaimura, Japan

International Nuclear Information System (INIS)

1999-01-01

Following the accident on 30 September 1999 at the nuclear fuel processing facility at Tokaimura, Japan, the IAEA Emergency Response Centre received numerous requests for information about the event's causes and consequences from Contact Points under the Conventions on Early Notification of a Nuclear Accident and on Assistance in the Case of a Nuclear Accident or Radiological Emergency. Although the lack of transboundary consequences of the accident meant that action under the Early Notification Convention was not triggered, the Emergency Response Centre issued several advisories to Member States which drew on official reports received from Japan. After discussions with the Government of Japan, the IAEA dispatched a team of three experts from the Secretariat on a fact finding mission to Tokaimura from 13 to 17 October 1999. The present preliminary report by that team documents key technical information obtained during the mission. At this stage, the report can in no way provide conclusive judgements on the causes and consequences of the accident. Investigations are proceeding in Japan and more information is expected to be made available after access has been gained to the building where the accident occurred. Moreover, much of the information already made available will be revised as more accurate assessments are made, for example of the radiation doses to the three individuals who received the highest exposures. Notwithstanding the preliminary nature of this report, it is clear that the accident was not one involving widespread contamination of the environment as in the 1986 Chernobyl accident. Although there was little risk off the site once the accident had been brought under control, the authorities evacuated the population living within a few hundred metres and advised people within about 10 km of the facility to take shelter for a period of about one day. The event at Tokaimura was nevertheless a serious industrial accident. The results of the detailed

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

Directory of Open Access Journals (Sweden)

Akpona Okujeni

2014-07-01

Full Text Available Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR, kernel ridge regression (KRR, artificial neural networks (NN, random forest regression (RFR and partial least squares regression (PLSR. Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN or limited (RFR and PLSR performances when comprehensively mapping urban land cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.

Healing of extraction sockets filled with BoneCeramic® prior to implant placement: preliminary histological findings.

Science.gov (United States)

De Coster, Peter; Browaeys, Hilde; De Bruyn, Hugo

2011-03-01

Various grafting materials have been designed to minimize edentulous ridge volume loss following tooth extraction by encouraging new bone formation in healing sockets. BoneCeramic® is a composite of hydroxyapatite and bèta-tricalcium phosphate with pores of 100-500 microns. The aim of this study was to evaluate bone regeneration in healing sockets substituted with BoneCeramic® prior to implant procedures. Fifteen extraction sockets were substituted with BoneCeramic® and 14 sockets were left to heal naturally in 10 patients (mean age 59.6 years). Biopsies were collected only from the implant recipient sites during surgery after healing periods ranging from 6-74 weeks (mean 22). In total, 24 biopsies were available; 10 from substituted and 14 from naturally healed sites. In one site, the implant was not placed intentionally and, in four substituted sites, implant placement had to be postponed due to inappropriate healing, hence from five sites biopsies were not available. Histological sections were examined by transmitted light microscope. At the time of implant surgery, bone at substituted sites was softer than in controls, compromising initial implant stability. New bone formation at substituted sites was consistently poorer than in controls, presenting predominantly loose connective tissue and less woven bone. The use of BoneCeramic® as a grafting material in fresh extraction sockets appears to interfere with normal healing processes of the alveolar bone. On the basis of the present preliminary findings, its indication as a material for bone augmentation, when implant placement is considered within 6-38 weeks after extraction, should be revised. © 2009, Copyright the Authors. Journal Compilation © 2011, Wiley Periodicals, Inc.

The Effect of a Sports Stadium on Housing Rents: An Application of Geographically Weighted Regression

Directory of Open Access Journals (Sweden)

Jorge Enrique Agudelo Torres

2015-06-01

Full Text Available Researchers have determined that real estate prices vary in continuous ways as a function of spatial characteristics.  In this study we examine whether geographically weighted regression (GWR provides different estimates of price effects around a sports stadium than more traditional regression techniques.  We find that an application of GWR with hedonic prices finds that the stadium has a negative external effect on housing rents that extends outward 560 meters, in contrast to the positive external effect on housing rents found using a conventional estimation technique.

Preliminary investigation of seasonality in the Great Berg Estuary

CSIR Research Space (South Africa)

Slinger, JH

1994-10-01

Full Text Available in summer, while salinities in excess of 5 x 10(-3) occur 37 km from the mouth. The role of river flow in counterbalancing the upstream dispersion of salt during the summer season is highlighted. The relevance of these findings in the preliminary...

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Economic viability in concrete dams by multivariable regression tool for implantation of small hydroelectric plants

International Nuclear Information System (INIS)

Lima, Reginaldo Agapito de; Ribeiro Junior, Leopoldo Uberto

2010-01-01

For implantation of a SHP, the barrage is the main structure where its sizing represents from 30% - 50% of general cost of civil works. Considering this it is very important to have a fast, didactic and accurate tool for elaborating a budget, also allowing a quantitative analysis of inherent cost for civil building of barrages concrete made for small hydropower plants. In face of this, the multi changing regression tool is very important as it allows a fast and correct establishing of preliminary costs, even approximate, for estimates of barrages in concrete cost, enabling to ease the budget, guiding feasibility decisions for selecting or neglecting new alternatives of fall. (author)

Risk factors for isolated sleep paralysis in an African American sample: a preliminary study.

Science.gov (United States)

Ramsawh, Holly J; Raffa, Susan D; White, Kamila S; Barlow, David H

2008-12-01

Isolated sleep paralysis (ISP) is a temporary period of involuntary immobility that can occur at sleep onset or offset. It has previously been reported in association with both panic disorder (PD) and posttraumatic stress disorder (PTSD). The current study examined the association between ISP and several possible risk factors--anxiety sensitivity, trauma exposure, life stress, and paranormal beliefs--in a sample of African American participants with and without a history of ISP. Significant between-group differences were found for PD and PTSD diagnoses, anxiety sensitivity, life stress, and certain aspects of paranormal belief, with the ISP group being higher on all of these indices. No differences were found with regard to trauma exposure. Hierarchical regression analyses indicated that PD, anxiety sensitivity, and life stress each contributed unique variance to ISP cognitive symptoms, whereas PTSD and paranormal beliefs did not. These results provide preliminary support for an association between ISP and anxiety sensitivity and corroborate previous reports of ISP's association with PD and life stress. The current trauma/PTSD findings are mixed, however, and warrant future research.

Caudal regression with sirenomelia and dysplasia renofacialis (Potter's syndrome)

Energy Technology Data Exchange (ETDEWEB)

Noeldge, G.; Billmann, P.; Boehm, N.

1982-05-01

A case of caudal regression in combination with sirenomelia and dysplasia renofacialis (Potter's syndrome) is reported. The formal pathogenesis of these malformations and clinical facts are shown and discussed. Findings of plain films, postmortal angiography and pathologic-anatomical changes are demonstrated.

77 FR 65171 - Fresh Garlic From the People's Republic of China: Preliminary Rescission of Antidumping Duty New...

Science.gov (United States)

2012-10-25

... detailed in the Preliminary Decision Memorandum, the Department finds that Fuyi's sales under review are...'s Republic of China: Preliminary Rescission of Antidumping Duty New Shipper Reviews; 2010-2011... Department of Commerce (Department) is conducting new shipper reviews (NSR) of the antidumping duty order on...

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

OpenAIRE

Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.

2016-01-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epide...

Face Alignment via Regressing Local Binary Features.

Science.gov (United States)

Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian

2016-03-01

This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.

Multivariate Frequency-Severity Regression Models in Insurance

Directory of Open Access Journals (Sweden)

Edward W. Frees

2016-02-01

Full Text Available In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one’s own vehicle, damage to another party’s vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i property; (ii motor vehicle; and (iii contractors’ equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

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.

Logic regression and its extensions.

Science.gov (United States)

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

Tumor regression patterns in retinoblastoma

International Nuclear Information System (INIS)

Zafar, S.N.; Siddique, S.N.; Zaheer, N.

2016-01-01

To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)

Sintering equation: determination of its coefficients by experiments - using multiple regression

International Nuclear Information System (INIS)

Windelberg, D.

1999-01-01

Sintering is a method for volume-compression (or volume-contraction) of powdered or grained material applying high temperature (less than the melting point of the material). Maekipirtti tried to find an equation which describes the process of sintering by its main parameters sintering time, sintering temperature and volume contracting. Such equation is called a sintering equation. It also contains some coefficients which characterise the behaviour of the material during the process of sintering. These coefficients have to be determined by experiments. Here we show that some linear regressions will produce wrong coefficients, but multiple regression results in an useful sintering equation. (orig.)

Combining Alphas via Bounded Regression

Directory of Open Access Journals (Sweden)

Zura Kakushadze

2015-11-01

Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.

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....

Regression in autistic spectrum disorders.

Science.gov (United States)

Stefanatos, Gerry A

2008-12-01

A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

Detecting overdispersion in count data: A zero-inflated Poisson regression analysis

Science.gov (United States)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah

2017-09-01

This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.

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...

Multivariate regression applied to the performance optimization of a countercurrent ultracentrifuge - a preliminary study

International Nuclear Information System (INIS)

Migliavacca, Elder; Andrade, Delvonei Alves de

2004-01-01

In this work, the least-squares methodology with covariance matrix is applied to determine a data curve fitting in order to obtain a performance function for the separative power δU of a ultracentrifuge as a function of variables that are experimentally controlled. The experimental data refer to 173 experiments on the ultracentrifugation process for uranium isotope separation. The experimental uncertainties related with these independent variables are considered in the calculation of the experimental separative power values, determining an experimental data input covariance matrix. The process control variables, which significantly influence the δU values, are chosen in order to give information on the ultracentrifuge behaviour when submitted to several levels of feed flow F and cut θ . After the model goodness-of-fit validation, a residual analysis is carried out to verify the assumed basis concerning its randomness and independence and mainly the existence of residual heterocedasticity with any regression model variable. The response curves are made relating the separative power with the control variables F and θ, to compare the fitted model with the experimental data and finally to calculate their optimized values. (author)

Regressão e crescimento do primogênito no processo de tornar-se irmão Firstborn's regression and growth in the process of becoming a sibling

Directory of Open Access Journals (Sweden)

Débora Silva Oliveira

2013-03-01

Full Text Available Investigaram-se indicadores de regressão e crescimento do primogênito no processo de tornar-se irmão. Participaram três primogênitos pré-escolares no terceiro trimestre de gestação, aos 12 e 24 meses do irmão. Foi aplicado o Teste das Fábulas e realizada análise qualitativa de conteúdo. Os resultados revelaram regressão do primogênito na gestação materna e crescimento, aos 12 e aos 24 meses de idade do irmão. A regressão foi uma forma de enfrentar a chegada do irmão, enquanto que o crescimento revelou capacidade para conquistas ou custos de ser mais velho. Tanto a regressão quanto o crescimento oportunizaram um ir e vir saudável, fundamental para o desenvolvimento rumo à independência. Esses achados têm implicações para a pesquisa e para a clínica.Regression and growth indicators in the process of becoming a sibling were investigated. Three firstborns took part in the study during the first sibling's third trimester of pregnancy, and when the sibling was 12 and 24 months old, respectively. The Fables Test was used and a qualitative content analysis was carried out. Results revealed regression indicators during pregnancy. At 12 and 24 months there were growth indicators together with regression indicators. Regression was used by the firstborn for coping with the sibling's arrival while growth revealed the capacity for acquisitions or the costs of being an older sibling. Both regressive and growth manifestations enabled a healthy to and fro, which is fundamental for development towards independence. These findings have both research and clinical implications.

Rape-related cognitive distortions: Preliminary findings on the role of early maladaptive schemas.

Science.gov (United States)

Sigre-Leirós, Vera; Carvalho, Joana; Nobre, Pedro J

2015-01-01

Despite the important focus on the notion of cognitive distortions in the sexual offending area, the relevance of underlying cognitive schemas in sexual offenders has also been suggested. The aim of the present study was to investigate a potential relationship between Early Maladaptive Schemas (EMSs) and cognitive distortions in rapists. A total of 33 men convicted for rape completed the Bumby Rape Scale (BRS), the Young Schema Questionnaire - Short form-3 (YSQ-S3), the Brief Symptom Inventory (BSI), and the Socially Desirable Response Set Measure (SDRS-5). Results showed a significant relationship between the impaired limits schematic domain and the Justifying Rape dimension of the BRS. Specifically, after controlling for psychological distress levels and social desirability tendency, the entitlement/grandiosity schema from the impaired limits domain was a significant predictor of cognitive distortions related to Justifying Rape themes. Overall, despite preliminary, there is some evidence that the Young's Schema-Focused model namely the impaired limits dimension may contribute for the conceptualization of cognitive distortions in rapists and further investigation is recommended. Copyright © 2015 Elsevier Ltd. All rights reserved.

Environmental Survey preliminary report

Energy Technology Data Exchange (ETDEWEB)

1988-04-01

This report presents the preliminary findings from the first phase of the Environmental Survey of the United States Department of Energy (DOE) Sandia National Laboratories conducted August 17 through September 4, 1987. The objective of the Survey is to identify environmental problems and areas of environmental risk associated with Sandia National Laboratories-Albuquerque (SNLA). The Survey covers all environmental media and all areas of environmental regulation. It is being performed in accordance with the DOE Environmental Survey Manual. This phase of the Survey involves the review of existing site environmental data, observations of the operations carried on at SNLA, and interviews with site personnel. 85 refs., 49 figs., 48 tabs.

Linear regression in astronomy. II

Science.gov (United States)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

A Matlab program for stepwise regression

Directory of Open Access Journals (Sweden)

Yanhong Qi

2016-03-01

Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.

Diagnostic Algorithm to Reflect Regressive Changes of Human Papilloma Virus in Tissue Biopsies

Science.gov (United States)

Lhee, Min Jin; Cha, Youn Jin; Bae, Jong Man; Kim, Young Tae

2014-01-01

Purpose Landmark indicators have not yet to be developed to detect the regression of cervical intraepithelial neoplasia (CIN). We propose that quantitative viral load and indicative histological criteria can be used to differentiate between atypical squamous cells of undetermined significance (ASCUS) and a CIN of grade 1. Materials and Methods We collected 115 tissue biopsies from women who tested positive for the human papilloma virus (HPV). Nine morphological parameters including nuclear size, perinuclear halo, hyperchromasia, typical koilocyte (TK), abortive koilocyte (AK), bi-/multi-nucleation, keratohyaline granules, inflammation, and dyskeratosis were examined for each case. Correlation analyses, cumulative logistic regression, and binary logistic regression were used to determine optimal cut-off values of HPV copy numbers. The parameters TK, perinuclear halo, multi-nucleation, and nuclear size were significantly correlated quantitatively to HPV copy number. Results An HPV loading number of 58.9 and AK number of 20 were optimal to discriminate between negative and subtle findings in biopsies. An HPV loading number of 271.49 and AK of 20 were optimal for discriminating between equivocal changes and obvious koilocytosis. Conclusion We propose that a squamous epithelial lesion with AK of >20 and quantitative HPV copy number between 58.9-271.49 represents a new spectrum of subtle pathological findings, characterized by AK in ASCUS. This can be described as a distinct entity and called "regressing koilocytosis". PMID:24532500

Studies on the radioactive contamination due to nuclear detonations II. Preliminary findings on the radioactive fallout due to nuclear detonations

Energy Technology Data Exchange (ETDEWEB)

Nishiwaki, Yasushi [Nuclear Reactor Laboratory, Tokyo Institute of Technology, Tokyo (Japan); Nuclear Reactor Laboratoroy, Kinki University, Fuse City, Osaka Precture (Japan)

1961-11-25

Since we have detected a considerable amount of artificial radioactivity in the rain in spring 1954, it has become one of the most important items, from the health physics point of view, to continue measurements of radioactivity in the rain and in the atmosphere. To watch out the radioactive contamination of our environment due to repeated nuclear weapons testings in other countries was also considered to be important from the nuclear engineering point of view, in the sense that the permissible allowances of the radioactivity for the peaceful uses of atomic energy might be lowered if the degree of radioactive contamination due to nuclear testings should continue to increase gradually and indefinitely. If the permissible level were lowered, the cost for radiation protection may be expected to increase at the peaceful uses of atomic energy and should the radioactive contamination increase seriously in the future, it was anticipated that we may have to face a very difficult situation in designing the atomic energy facilities for peaceful purposes in our country. From these points of views, we have been continuing measurements of the radioactivity in the rain in Osaka, Japan since the spring of 1954. Some of the preliminary findings are introduced in this paper.

Studies on the radioactive contamination due to nuclear detonations II. Preliminary findings on the radioactive fallout due to nuclear detonations

International Nuclear Information System (INIS)

Nishiwaki, Yasushi

1961-01-01

Since we have detected a considerable amount of artificial radioactivity in the rain in spring 1954, it has become one of the most important items, from the health physics point of view, to continue measurements of radioactivity in the rain and in the atmosphere. To watch out the radioactive contamination of our environment due to repeated nuclear weapons testings in other countries was also considered to be important from the nuclear engineering point of view, in the sense that the permissible allowances of the radioactivity for the peaceful uses of atomic energy might be lowered if the degree of radioactive contamination due to nuclear testings should continue to increase gradually and indefinitely. If the permissible level were lowered, the cost for radiation protection may be expected to increase at the peaceful uses of atomic energy and should the radioactive contamination increase seriously in the future, it was anticipated that we may have to face a very difficult situation in designing the atomic energy facilities for peaceful purposes in our country. From these points of views, we have been continuing measurements of the radioactivity in the rain in Osaka, Japan since the spring of 1954. Some of the preliminary findings are introduced in this paper

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

Fungible weights in logistic regression.

Science.gov (United States)

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Investing in Global Markets: Big Data and Applications of Robust Regression

Directory of Open Access Journals (Sweden)

John eGuerard

2016-02-01

Full Text Available In this analysis of the risk and return of stocks in global markets, we apply several applications of robust regression techniques in producing stock selection models and several optimization techniques in portfolio construction in global stock universes. We find that (1 the robust regression applications are appropriate for modeling stock returns in global markets; and (2 mean-variance techniques continue to produce portfolios capable of generating excess returns above transaction costs and statistically significant asset selection. We estimate expected return models in a global equity markets using a given stock selection model and generate statistically significant active returns from various portfolio construction techniques.

Environmental Survey preliminary report, Sandia National Laboratories, Livermore, California

International Nuclear Information System (INIS)

1988-01-01

This report contains the preliminary findings based on the first phase of an Environmental Survey at the Department of Energy (DOE) Sandia National Laboratories Livermore (SNLL), located at Livermore, California. The Survey is being conducted by DOE's Office of Environment, Safety and Health. The SNLL Survey is a portion of the larger, comprehensive DOE Environmental Survey encompassing all major operating facilities of DOE. The DOE Environmental Survey is one of a series of initiatives announced on September 18, 1985, by Secretary of Energy, John S. Herrington, to strengthen the environmental, safety, and health programs and activities within DOE. The purpose of the Environmental Survey is to identify, via a ''no fault'' baseline Survey of all the Department's major operating facilities, environmental problems and areas of environmental risk. The identified problem areas will be prioritized on a Department-wide basis in order of importance in 1989. The findings in this report are subject to modification based on the results from the Sampling and Analysis Phase of the Survey. The findings are also subject to modification based on comments from the Albuquerque Operations Office concerning the technical accuracy of the findings. The modified preliminary findings and any other appropriate changes will be incorporated into an Interim Report. The Interim Report will serve as the site-specific source for environmental information generated by the Survey, and ultimately as the primary source of information for the DOE-wide prioritization of environmental problems in the Survey Summary Report. 43 refs., 21 figs., 24 tabs

Environmental Survey preliminary report, Sandia National Laboratories, Livermore, California

Energy Technology Data Exchange (ETDEWEB)

1988-01-01

This report contains the preliminary findings based on the first phase of an Environmental Survey at the Department of Energy (DOE) Sandia National Laboratories Livermore (SNLL), located at Livermore, California. The Survey is being conducted by DOE's Office of Environment, Safety and Health. The SNLL Survey is a portion of the larger, comprehensive DOE Environmental Survey encompassing all major operating facilities of DOE. The DOE Environmental Survey is one of a series of initiatives announced on September 18, 1985, by Secretary of Energy, John S. Herrington, to strengthen the environmental, safety, and health programs and activities within DOE. The purpose of the Environmental Survey is to identify, via a no fault'' baseline Survey of all the Department's major operating facilities, environmental problems and areas of environmental risk. The identified problem areas will be prioritized on a Department-wide basis in order of importance in 1989. The findings in this report are subject to modification based on the results from the Sampling and Analysis Phase of the Survey. The findings are also subject to modification based on comments from the Albuquerque Operations Office concerning the technical accuracy of the findings. The modified preliminary findings and any other appropriate changes will be incorporated into an Interim Report. The Interim Report will serve as the site-specific source for environmental information generated by the Survey, and ultimately as the primary source of information for the DOE-wide prioritization of environmental problems in the Survey Summary Report. 43 refs., 21 figs., 24 tabs.

Principal component regression analysis with SPSS.

Science.gov (United States)

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Ethanolic extract of Artemisia aucheri induces regression of aorta wall fatty streaks in hypercholesterolemic rabbits.

Science.gov (United States)

Asgary, S; Dinani, N Jafari; Madani, H; Mahzouni, P

2008-05-01

Artemisia aucheri is a native-growing plant which is widely used in Iranian traditional medicine. This study was designed to evaluate the effects of A. aucheri on regression of atherosclerosis in hypercholesterolemic rabbits. Twenty five rabbits were randomly divided into five groups of five each and treated 3-months as follows: 1: normal diet, 2: hypercholesterolemic diet (HCD), 3 and 4: HCD for 60 days and then normal diet and normal diet + A. aucheri (100 mg x kg(-1) x day(-1)) respectively for an additional 30 days (regression period). In the regression period dietary use of A. aucheri in group 4 significantly decreased total cholesterol, triglyceride and LDL-cholesterol, while HDL-cholesterol was significantly increased. The atherosclerotic area was significantly decreased in this group. Animals, which received only normal diet in the regression period showed no regression but rather progression of atherosclerosis. These findings suggest that A. aucheri may cause regression of atherosclerotic lesions.

An Analysis of Bank Service Satisfaction Based on Quantile Regression and Grey Relational Analysis

Directory of Open Access Journals (Sweden)

Wen-Tsao Pan

2016-01-01

Full Text Available Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures.

Logistic regression models

CERN Document Server

Hilbe, Joseph M

2009-01-01

This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...

Logistic regression applied to natural hazards: rare event logistic regression with replications

Science.gov (United States)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

Science.gov (United States)

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity

Variable Selection for Regression Models of Percentile Flows

Science.gov (United States)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

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.

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.

The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics

Directory of Open Access Journals (Sweden)

Ronald de Vlaming

2015-01-01

Full Text Available In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i the theoretical foundations of ridge regression, (ii its link to commonly used methods in animal breeding, (iii the computational feasibility, and (iv the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis. Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N<10,000 the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP. However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.

Preliminary Validation of the Child Abuse Potential Inventory in Turkey

Science.gov (United States)

Kutsal, Ebru; Pasli, Figen; Isikli, Sedat; Sahin, Figen; Yilmaz, Gokce; Beyazova, Ufuk

2011-01-01

This study aims to provide preliminary findings on the validity of Child Abuse Potential Inventory (CAP Inventory) on Turkish sample of 23 abuser and 47 nonabuser parents. To investigate validity in two groups, Minnesota Multiphasic Personality Inventory (MMPI) Psychopathic Deviate (MMPI-PD) scale is also used along with CAP. The results show…

Volume of discrete brain structures in complex dissociative disorders : preliminary findings

NARCIS (Netherlands)

Ehling, T.; Nijenhuis, E. R. S.; Krikke, A. P.; DeKloet, ER; Vermetten, E

2007-01-01

Based on findings in traumatized animals and patients with posttraumatic stress disorder, and on traumatogenic models of complex dissociative disorders, it was hypothesized that (1) patients with complex dissociative disorders have smaller volumes of hippocampus, parahippocampal gyrus, and amygdala

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....

Lingual–Alveolar Contact Pressure During Speech in Amyotrophic Lateral Sclerosis: Preliminary Findings

Science.gov (United States)

Knollhoff, Stephanie; Barohn, Richard J.

2017-01-01

Purpose This preliminary study on lingual–alveolar contact pressures (LACP) in people with amyotrophic lateral sclerosis (ALS) had several aims: (a) to evaluate whether the protocol induced fatigue, (b) to compare LACP during speech (LACP-Sp) and during maximum isometric pressing (LACP-Max) in people with ALS (PALS) versus healthy controls, (c) to compare the percentage of LACP-Max utilized during speech (%Max) for PALS versus controls, and (d) to evaluate relationships between LACP-Sp and LACP-Max with word intelligibility. Method Thirteen PALS and 12 healthy volunteers produced /t, d, s, z, l, n/ sounds while LACP-Sp was recorded. LACP-Max was obtained before and after the speech protocol. Word intelligibility was obtained from auditory–perceptual judgments. Results LACP-Max values measured before and after completion of the speech protocol did not differ. LACP-Sp and LACP-Max were statistically lower in the ALS bulbar group compared with controls and PALS with only spinal symptoms. There was no statistical difference between groups for %Max. LACP-Sp and LACP-Max were correlated with word intelligibility. Conclusions It was feasible to obtain LACP-Sp measures without inducing fatigue. Reductions in LACP-Sp and LACP-Max for bulbar speakers might reflect tongue weakness. Although confirmation of results is needed, the data indicate that individuals with high word intelligibility maintained LACP-Sp at or above 2 kPa and LACP-Max at or above 50 kPa. PMID:28335033

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...

Logistic regression applied to natural hazards: rare event logistic regression with replications

Directory of Open Access Journals (Sweden)

M. Guns

2012-06-01

Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

Science.gov (United States)

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

The U.S. Forest Service abandoned mine land inventory in Colorado: Background, progress, and preliminary findings

International Nuclear Information System (INIS)

Sares, M.A.

1996-01-01

The U.S. Forest Service (USFS) and the Colorado Geological Survey (CGS) are continuing a cooperative agreement to identify sites of environmental degradation associated with abandoned and inactive mines on Colorado's USFS administered lands. The USFS Abandoned Mine Land Inventory Project is a open-quotes discoveryclose quotes process and is a precursor to the Environmental Protection Agency's open-quotes Preliminary Assessmentclose quotes process. Identification of environmentally degraded sites may lead to a formal Preliminary Assessment. The inventory process begins in the office and involves reviewing existing mining and geologic literature, previous mine inventory work, current and historical maps, water quality information, and aerial photographs. During field investigation, each mine feature is given a unique identification number. Field geologists collect data on the physical and geographic characteristics of the mine features along with information on any water emanating from or interacting with the mine features. This information is used to assign a qualitative environmental degradation rating to the individual mine feature. Guidelines for the rating system are given to field personnel to facilitate consistency within the data set. All data collected are entered into a computer database. From a computer perspective, both location and attribute data are being collected. Therefore, the data are well suited for integration into a geographic information system (GIS) creating a geo-referenced data set. The USFS Abandoned Mine Land Inventory Project began in 1991 and is ongoing. To date, field inventories of the Arapaho, Roosevelt, Pike, and Rio Grande National Forests have been completed. Work in the San Isabel, San Juan, White River, Gunnison, Uncompahgre, and Grand Mesa National Forests is in progress. Through the 1994 field season approximately 9,667 mine features (openings, dumps, tailings, highwalls, etc.) have been inventoried

Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models.

Science.gov (United States)

Chen, Baojiang; Qin, Jing

2014-05-10

In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.

Post-processing through linear regression

Science.gov (United States)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

[Virtual bronchoscopy: the correlation between endoscopic simulation and bronchoscopic findings].

Science.gov (United States)

Salvolini, L; Gasparini, S; Baldelli, S; Bichi Secchi, E; Amici, F

1997-11-01

We carried out a preliminary clinical validation of 3D spiral CT virtual endoscopic reconstructions of the tracheobronchial tree, by comparing virtual bronchoscopic images with actual endoscopic findings. Twenty-two patients with tracheobronchial disease suspected at preliminary clinical, cytopathological and plain chest film findings were submitted to spiral CT of the chest and bronchoscopy. CT was repeated after endobronchial therapy in 2 cases. Virtual endoscopic shaded-surface-display views of the tracheobronchial tree were reconstructed from reformatted CT data with an Advantage Navigator software. Virtual bronchoscopic images were preliminarily evaluated with a semi-quantitative quality score (excellent/good/fair/poor). The depiction of consecutive airway branches was then considered. Virtual bronchoscopies were finally submitted to double-blind comparison with actual endoscopies. Virtual image quality was considered excellent in 8 cases, good in 14 and fair in 2. Virtual exploration was stopped at the lobar bronchi in one case only; the origin of segmental bronchi was depicted in 23 cases and that of some subsegmental branches in 2 cases. Agreement between actual and virtual bronchoscopic findings was good in all cases but 3 where it was nevertheless considered satisfactory. The yield of clinically useful information differed in 8/24 cases: virtual reconstructions provided more information than bronchoscopy in 5 cases and vice versa in 3. Virtual reconstructions are limited in that the procedure is long and difficult and needing a strictly standardized threshold value not to alter virtual findings. Moreover, the reconstructed surface lacks transparency, there is the partial volume effect and the branches < or = 4 pixels phi and/or meandering ones are difficult to explore. Our preliminary data are encouraging. Segmental bronchi were depicted in nearly all cases, except for the branches involved by disease. Obstructing lesions could be bypassed in some cases

A comparison of the recruitment of antibody forming cells in the nose and lung: Preliminary findings

Energy Technology Data Exchange (ETDEWEB)

King-Herbert, A P; Bice, D E; Harkema, J R

1988-12-01

Instillation of a particulate antigen into a selected lung lobe leads to an accumulation of antibody forming cells in the exposed lung lobe. Our goal in this preliminary study was to determine if an immune response could be elicited in the nasal mucosa of Beagle dogs exposed to a particulate antigen, and if so, to compare this immune response with that of the lungs when the nasal mucosa and the lungs are each immunized with a different particulate antigen. An Immune response was observed when the nasal mucosa was exposed to particulate antigen, but numbers of antibody-forming cells and levels of antibody in the nose were much lower than observed in an immunized lung lobe. (author)

A comparison of the recruitment of antibody forming cells in the nose and lung: Preliminary findings

International Nuclear Information System (INIS)

King-Herbert, A.P.; Bice, D.E.; Harkema, J.R.

1988-01-01

Instillation of a particulate antigen into a selected lung lobe leads to an accumulation of antibody forming cells in the exposed lung lobe. Our goal in this preliminary study was to determine if an immune response could be elicited in the nasal mucosa of Beagle dogs exposed to a particulate antigen, and if so, to compare this immune response with that of the lungs when the nasal mucosa and the lungs are each immunized with a different particulate antigen. An Immune response was observed when the nasal mucosa was exposed to particulate antigen, but numbers of antibody-forming cells and levels of antibody in the nose were much lower than observed in an immunized lung lobe. (author)

Regression modeling methods, theory, and computation with SAS

CERN Document Server

Panik, Michael

2009-01-01

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

Better Autologistic Regression

Directory of Open Access Journals (Sweden)

Mark A. Wolters

2017-11-01

Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.

Diffusion-weighted MR imaging of cystic lesions of neurocysticercosis: a preliminary study

International Nuclear Information System (INIS)

Raffin, Luciana S.; Bacheschi, Luiz A.; Machado, Luis R.; Nobrega, Jose P.S.; Coelho, Christina; Leite, Claudia C.

2001-01-01

Neurocysticercosis is an endemic disease in some developing countries. It has pleomorfic clinical and imaging findings, which are variable from patient to patient. In this preliminary note, we studied the magnetic resonance diffusion-weighted images of sixteen patients presenting with cystic lesions of this disease diagnosed by clinical and laboratorial findings. All the lesions had hypointense signal and the similar apparent diffusion coefficient values as the cerebrospinal fluid. (author)

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...

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

Science.gov (United States)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Interpretation of commonly used statistical regression models.

Science.gov (United States)

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

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...

Regression modeling of ground-water flow

Science.gov (United States)

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Natalizumab Significantly Improves Cognitive Impairment over Three Years in MS: Pattern of Disability Progression and Preliminary MRI Findings.

Directory of Open Access Journals (Sweden)

Flavia Mattioli

Full Text Available Previous studies reported that Multiple Sclerosis (MS patients treated with natalizumab for one or two years exhibit a significant reduction in relapse rate and in cognitive impairment, but the long term effects on cognitive performance are unknown. This study aimed to evaluate the effects of natalizumab on cognitive impairment in a cohort of 24 consecutive patients with relapsing remitting MS treated for 3 years. The neuropsychological tests, as well as relapse number and EDSS, were assessed at baseline and yearly for three years. The impact on cortical atrophy was also considered in a subgroup of them, and are thus to be considered as preliminary. Results showed a significant reduction in the number of impaired neuropsychological tests after three years, a significant decrease in annualized relapse rate at each time points compared to baseline and a stable EDSS. In the neuropsychological assessment, a significant improvement in memory, attention and executive function test scores was detected. Preliminary MRI data show that, while GM volume did not change at 3 years, a significantly greater parahippocampal and prefrontal gray matter density was noticed, the former correlating with neuropsychological improvement in a memory test. This study showed that therapy with Natalizumab is helpful in improving cognitive performance, and is likely to have a protective role on grey matter, over a three years follow-up.

Use of activity theory-based need finding for biomedical device development.

Science.gov (United States)

Rismani, Shalaleh; Ratto, Matt; Machiel Van der Loos, H F

2016-08-01

Identifying the appropriate needs for biomedical device design is challenging, especially for less structured environments. The paper proposes an alternate need-finding method based on Cultural Historical Activity Theory and expanded to explicitly examine the role of devices within a socioeconomic system. This is compared to a conventional need-finding technique in a preliminary study with engineering student teams. The initial results show that the Activity Theory-based technique allows teams to gain deeper insights into their needs space.

Post-processing through linear regression

Directory of Open Access Journals (Sweden)

B. Van Schaeybroeck

2011-03-01

Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.

These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

Science.gov (United States)

Smith, Paul F; Ganesh, Siva; Liu, Ping

2013-10-30

Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

Logistic regression applied to natural hazards: rare event logistic regression with replications

OpenAIRE

Guns, M.; Vanacker, Veerle

2012-01-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...

Preliminary Monthly Climatological Summaries

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary Local Climatological Data, recorded since 1970 on Weather Burean Form 1030 and then National Weather Service Form F-6. The preliminary climate data pages...

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.

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.

CT and the diagnosis of myopathies. Preliminary findings in 42 cases

Energy Technology Data Exchange (ETDEWEB)

Calgo, M; Crisi, G; Martinelli, C; Colombo, A; Schoenhuber, R; Gibertoni, M

1986-01-01

A total of 42 patients with myopathies underwent CT scans in order to study the relationship between CT images and clinical findings. CT is a valuable diagnostic aid to distinguish primary from neurogenic myopathies, to facilitate directed biopsy and finally to classify the disease according to the degree and extent of the muscular lesion. (orig.).

Recursive Algorithm For Linear Regression

Science.gov (United States)

Varanasi, S. V.

1988-01-01

Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.

Health care: necessity or luxury good? A meta-regression analysis

OpenAIRE

Iordache, Ioana Raluca

2014-01-01

When estimating the influence income per capita exerts on health care expenditure, the research in the field offers mixed results. Studies employ different data, estimation techniques and models, which brings about the question whether these differences in research design play any part in explaining the heterogeneity of reported outcomes. By employing meta-regression analysis, the present paper analyzes 220 estimates of health spending income elasticity collected from 54 studies and finds tha...

Applied regression analysis a research tool

CERN Document Server

Pantula, Sastry; Dickey, David

1998-01-01

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...

State ownership and corporate performance: A quantile regression analysis of Chinese listed companies

NARCIS (Netherlands)

Li, T.; Sun, L.; Zou, L.

2009-01-01

This study assesses the impact of government shareholding on corporate performance using a sample of 643 non-financial companies listed on the Chinese stock exchanges. In view of the controversial empirical findings in the literature and the limitations of the least squares regressions, we adopt the

Standards for Standardized Logistic Regression Coefficients

Science.gov (United States)

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

Science.gov (United States)

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

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.

Using synthetic data to evaluate multiple regression and principal component analyses for statistical modeling of daily building energy consumption

Energy Technology Data Exchange (ETDEWEB)

Reddy, T.A. (Energy Systems Lab., Texas A and M Univ., College Station, TX (United States)); Claridge, D.E. (Energy Systems Lab., Texas A and M Univ., College Station, TX (United States))

1994-01-01

Multiple regression modeling of monitored building energy use data is often faulted as a reliable means of predicting energy use on the grounds that multicollinearity between the regressor variables can lead both to improper interpretation of the relative importance of the various physical regressor parameters and to a model with unstable regressor coefficients. Principal component analysis (PCA) has the potential to overcome such drawbacks. While a few case studies have already attempted to apply this technique to building energy data, the objectives of this study were to make a broader evaluation of PCA and multiple regression analysis (MRA) and to establish guidelines under which one approach is preferable to the other. Four geographic locations in the US with different climatic conditions were selected and synthetic data sequence representative of daily energy use in large institutional buildings were generated in each location using a linear model with outdoor temperature, outdoor specific humidity and solar radiation as the three regression variables. MRA and PCA approaches were then applied to these data sets and their relative performances were compared. Conditions under which PCA seems to perform better than MRA were identified and preliminary recommendations on the use of either modeling approach formulated. (orig.)

Preliminary 2D design study for A ampersand PCT

International Nuclear Information System (INIS)

Keto, E.; Azevedo, S.; Roberson, P.

1995-03-01

Lawrence Livermore National Laboratory is currently designing and constructing a tomographic scanner to obtain the most accurate possible assays of radioactivity in barrels of nuclear waste in a limited amount of time. This study demonstrates a method to explore different designs using laboratory experiments and numerical simulations. In particular, we examine the trade-off between spatial resolution and signal-to-noise. The simulations are conducted in two dimensions as a preliminary study for three dimensional imaging. We find that the optimal design is entirely dependent on the expected source sizes and activities. For nuclear waste barrels, preliminary results indicate that collimators with widths of 1 to 3 inch and aspect ratios of 5:1 to 10:1 should perform well. This type of study will be repeated in 3D in more detail to optimize the final design

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.

Mechanisms of neuroblastoma regression

Science.gov (United States)

Brodeur, Garrett M.; Bagatell, Rochelle

2014-01-01

Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179

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.

Multicollinearity and Regression Analysis

Science.gov (United States)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

Panel Smooth Transition Regression Models

DEFF Research Database (Denmark)

González, Andrés; Terasvirta, Timo; Dijk, Dick van

We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

Science.gov (United States)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross

Using the Logistic Regression model in supporting decisions of establishing marketing strategies

Directory of Open Access Journals (Sweden)

Cristinel CONSTANTIN

2015-12-01

Full Text Available This paper is about an instrumental research regarding the using of Logistic Regression model for data analysis in marketing research. The decision makers inside different organisation need relevant information to support their decisions regarding the marketing strategies. The data provided by marketing research could be computed in various ways but the multivariate data analysis models can enhance the utility of the information. Among these models we can find the Logistic Regression model, which is used for dichotomous variables. Our research is based on explanation the utility of this model and interpretation of the resulted information in order to help practitioners and researchers to use it in their future investigations

Credit Scoring Problem Based on Regression Analysis

OpenAIRE

Khassawneh, Bashar Suhil Jad Allah

2014-01-01

ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

The process and utility of classification and regression tree methodology in nursing research.

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Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-06-01

This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Discussion paper. English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. © 2013 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

How much complexity is warranted in a rainfall-runoff model? Findings obtained from symbolic regression, using Eureqa

Science.gov (United States)

Abrahart, R. J.; Beriro, D. J.

2012-04-01

The information content in a rainfall-runoff record is sufficient to support models of only very limited complexity (Jakeman and Hornberger, 1993). This begs the question of what limits should observed data place on the allowable complexity of rainfall-runoff models? Eureqa1 (Schmidt and Lipson, 2009) - pronounced "eureka" - is a software tool for finding equations and detecting mathematical relationships in a dataset. The challenge, for both software and modeller, is to identify, by means of symbolic regression, the simplest mathematical formulas which describe the underlying mechanisms that produced the data. It actually delivers, however, a series of preferred modelling solutions comprising one champion for each specific level of complexity i.e. related to solution enlargement involving the progressive incorporation of additional permitted factors (internal operators/ external drivers). The potential benefit of increased complexity can as a result be assessed in a rational manner. Eureqa is free to download and use; and, in the current study, has been employed to construct a set of rainfall-runoff transfer function models for the Annapolis River at Wilmot, in north-western Nova Scotia, Canada. The climatic conditions in this catchment present an interesting set of modelling challenges; daily variations and seasonal changes in temperature, snowfall and retention result in great difficulty for runoff prediction by means of a data-driven approach. Data from 10 years of daily observations are used in the present study (01/01/2000-31/12/2009): comprising [i] discharge, [ii] total rainfall (excluding snowfall), [iii] total snowfall, [iv] thickness of snow cover, and [v] maximum and [vi] minimum temperature. Precipitation occurs throughout the whole year being slightly lower during summer. Snowfall is common from November until April and rare hurricane weather may occur in autumn. The average maximum temperature is below 0 0C in January and February, but significant

Granulomatous mastitis: radiological findings

International Nuclear Information System (INIS)

Ozturk, M.; Mavili, E.; Kahriman, G.; Akcan, A.C.; Ozturk, F.

2007-01-01

Purpose: To evaluate the radiological, ultrasonographic, and magnetic resonance imaging (MRI) findings of idiopathic granulomatous mastitis. Material and Methods: Between April 2002 and June 2005, the mammography, ultrasound, color Doppler ultrasound, non enhanced MR, and dynamic MR findings of nine patients with the preliminary clinical diagnosis of malignancy and the final diagnosis of granulomatous mastitis were evaluated. Results: On mammography, asymmetrical focal densities with no distinct margins, ill-defined masses with spiculated contours, and bilateral multiple ill-defined nodules were seen. On ultrasound, in four patients a discrete, heterogenous hypoechoic mass, in two patients multiple abscesses, in one patient bilateral multiple central hypo peripheral hyperechoic lesions, in two patients heterogeneous hypo- and hyperechoic areas together with parenchymal distortion, and in one patient irregular hypoechoic masses with tubular extensions and abscess cavities were seen. Five of the lesions were vascular on color Doppler ultrasound. On MR mammography, the most frequent finding was focal or diffuse asymmetrical signal intensity changes that were hypointense on T1W images and hyperintense on T2W images, without significant mass effect. Nodular lesions were also seen. On dynamic contrast-enhanced mammography, mass-like enhancement, ring-like enhancement, and nodular enhancement were seen. The time-intensity curves differed from patient to patient and from lesion to lesion. Conclusion: The imaging findings of idiopathic granulomatous mastitis have a wide spectrum, and they are inconclusive for differentiating malignant and benign lesions

Granulomatous mastitis: radiological findings

Energy Technology Data Exchange (ETDEWEB)

Ozturk, M.; Mavili, E.; Kahriman, G.; Akcan, A.C.; Ozturk, F. [Depts. of Radiology, Surgery, and Pathology, Erciyes Univ. Medical Faculty, Kayseri (Turkey)

2007-02-15

Purpose: To evaluate the radiological, ultrasonographic, and magnetic resonance imaging (MRI) findings of idiopathic granulomatous mastitis. Material and Methods: Between April 2002 and June 2005, the mammography, ultrasound, color Doppler ultrasound, non enhanced MR, and dynamic MR findings of nine patients with the preliminary clinical diagnosis of malignancy and the final diagnosis of granulomatous mastitis were evaluated. Results: On mammography, asymmetrical focal densities with no distinct margins, ill-defined masses with spiculated contours, and bilateral multiple ill-defined nodules were seen. On ultrasound, in four patients a discrete, heterogenous hypoechoic mass, in two patients multiple abscesses, in one patient bilateral multiple central hypo peripheral hyperechoic lesions, in two patients heterogeneous hypo- and hyperechoic areas together with parenchymal distortion, and in one patient irregular hypoechoic masses with tubular extensions and abscess cavities were seen. Five of the lesions were vascular on color Doppler ultrasound. On MR mammography, the most frequent finding was focal or diffuse asymmetrical signal intensity changes that were hypointense on T1W images and hyperintense on T2W images, without significant mass effect. Nodular lesions were also seen. On dynamic contrast-enhanced mammography, mass-like enhancement, ring-like enhancement, and nodular enhancement were seen. The time-intensity curves differed from patient to patient and from lesion to lesion. Conclusion: The imaging findings of idiopathic granulomatous mastitis have a wide spectrum, and they are inconclusive for differentiating malignant and benign lesions.

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...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...

[From clinical judgment to linear regression model.

Science.gov (United States)

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

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…

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.

Discriminative Elastic-Net Regularized Linear Regression.

Science.gov (United States)

Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen

2017-03-01

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

Longitudinal strain predicts left ventricular mass regression after aortic valve replacement for severe aortic stenosis and preserved left ventricular function.

Science.gov (United States)

Gelsomino, Sandro; Lucà, Fabiana; Parise, Orlando; Lorusso, Roberto; Rao, Carmelo Massimiliano; Vizzardi, Enrico; Gensini, Gian Franco; Maessen, Jos G

2013-11-01

We explored the influence of global longitudinal strain (GLS) measured with two-dimensional speckle-tracking echocardiography on left ventricular mass regression (LVMR) in patients with pure aortic stenosis (AS) and normal left ventricular function undergoing aortic valve replacement (AVR). The study population included 83 patients with severe AS (aortic valve area regression (all P regression in patients with pure AS undergoing AVR. Our findings must be confirmed by further larger studies.

Quantile regression analysis of body mass and wages.

Science.gov (United States)

Johar, Meliyanni; Katayama, Hajime

2012-05-01

Using the National Longitudinal Survey of Youth 1979, we explore the relationship between body mass and wages. We use quantile regression to provide a broad description of the relationship across the wage distribution. We also allow the relationship to vary by the degree of social skills involved in different jobs. Our results find that for female workers body mass and wages are negatively correlated at all points in their wage distribution. The strength of the relationship is larger at higher-wage levels. For male workers, the relationship is relatively constant across wage distribution but heterogeneous across ethnic groups. When controlling for the endogeneity of body mass, we find that additional body mass has a negative causal impact on the wages of white females earning more than the median wages and of white males around the median wages. Among these workers, the wage penalties are larger for those employed in jobs that require extensive social skills. These findings may suggest that labor markets reward white workers for good physical shape differently, depending on the level of wages and the type of job a worker has. Copyright © 2011 John Wiley & Sons, Ltd.

A case of intracranial malignant lymphoma with pure akinesia and repeated regression on CT scans

International Nuclear Information System (INIS)

Suzuki, Takeo; Yamamoto, Mari; Saitoh, Mitsunori; Aoki, Akira; Imai, Hisamasa; Narabayashi, Hirotaro.

1984-01-01

In a case of primary reticulum cell sarcoma in the brain, histologically verified by biopsy, the tumor regressed twice on a CT scan without radiotherapy. The systemic freezing phenomenon was seen as a main clinical symptom. The patient, a 44 year-old male, first complained of decreased livido and festinating speech. He also showed frozen gait, micrographia, a decrease in spontaneity and urinary incontinence. Four months after onset he was hospitalized. Neurological findings on admission revealed freezing of gait, writing, and speech, but there was no weakness of muscles with normal tendon reflexes, and normal muscular tone. In the CT scan on admission, there were high density areas mainly in the head of the right caudate nucleus, the medial deep portion of the right frontal lobe, the right side of the hypothalamus, the anterior thalamus, the globus pallidus. There were also nodular-type enhanced effects in the same areas. Regression of the tumor was seen on the CT scans after administration of betamethasone. The tumor which had again incrased in size regressed spontaneously without the use of steroids after 3 months. Thereafter, the tumor gradually became larger and an open biopsy was perfomed. Histopathological findings showed a reticulum cell sarcoma. There were no findings of systemic malignant lymphoma. Such intracrainal malignant lymphomas showing repeated regression including spontaneous one are very rare in the literature. The freezing phenomenon in this case started with festinating speech and spread to writing and gait. L-DOPA had no effect. This systemic freezing phenomenon was considered to be the same as that in the cases of pure akinesia without rigidity and tremor reported by Narabayashi and Imai, which did not respond to L-DOPA at all. But on the other hand, L-Threo-3, 4-Dihydroxyphenylserine was effective to the frozen gait of this patient. (J.P.N.)

PM10 modeling in the Oviedo urban area (Northern Spain) by using multivariate adaptive regression splines

Science.gov (United States)

Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza

2014-10-01

The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of

Categorical regression dose-response modeling

Science.gov (United States)

The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...

Abstract Expression Grammar Symbolic Regression

Science.gov (United States)

Korns, Michael F.

This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.

Inferring gene expression dynamics via functional regression analysis

Directory of Open Access Journals (Sweden)

Leng Xiaoyan

2008-01-01

Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

Replica analysis of overfitting in regression models for time-to-event data

Science.gov (United States)

Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.

2017-09-01

Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.

Comparison of Classical Linear Regression and Orthogonal Regression According to the Sum of Squares Perpendicular Distances

OpenAIRE

KELEŞ, Taliha; ALTUN, Murat

2016-01-01

Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...

Pathological assessment of liver fibrosis regression

Directory of Open Access Journals (Sweden)

WANG Bingqiong

2017-03-01

Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.

Computing group cardinality constraint solutions for logistic regression problems.

Science.gov (United States)

Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

2017-01-01

We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.

The use of multicriteria decision making methods to find the environmental costs of hydropower development alternatives

International Nuclear Information System (INIS)

Carlsen, A.J.; Wenstoep, F.

1994-01-01

The conference paper deals with a decision support system (DSS) developed to find the costs of environmental goods. The system is based on multicriteria decision making and uses pairwise comparisons of two and two criteria. The criteria weights are calculated with linear regression. When one criterion is monetary, all criteria weights can be expressed in monetary units when the weights are known. The DSS has been tested on a hydropower project in the area of Sauda in Norway. To represent the decision makers, three panels each consisting of three persons were formed. The persons were selected from governmental agencies, the developers, the local environmental administration and a local politician. The DSS worked well with the panels. One problem was that impacts of hydropower projects are very site specific and also hard to quantify. Therefore, a considerable amount of time was used in creating a cognitive understanding of the issues involved and how they were represented by quantitative criteria. Some had also difficulties in accepting the principle of expressing environmental goods in monetary units. The results so far are preliminary. This research work is part of the Norwegian research programme Energy, Environment and Development. 3 refs., 4 figs., 2 tabs

Logistic Regression: Concept and Application

Science.gov (United States)

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

Science.gov (United States)

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

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.

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.

BIPS-FS preliminary design, miscellaneous notes

International Nuclear Information System (INIS)

1976-01-01

A compendium of flight system preliminary design internal memos and progress report extracts for the Brayton Isotope Power System Preliminary Design Review to be held July 20, 21, and 22, 1975 is presented. The purpose is to bring together those published items which relate only to the preliminary design of the Flight System, Task 2 of Phase I. This preliminary design effort was required to ensure that the Ground Demonstration System will represent the Flight System as closely as possible

Polymerase chain reaction in the diagnosis of tuberculous meningitis: preliminary report

Directory of Open Access Journals (Sweden)

L.R. Machado

1994-09-01

Full Text Available In this preliminary report the results of PCR for detection of DNA sequences (65 KDa antigen of Mycobacterium tuberculosis in CSF samples from 20 patients are registered. In 10 patients there were clinical and laboratory findings suggesting the diagnosis of tuberculous meningitis (test group. In the other 10 patients, clinical and laboratory findings suggested meningitis or meningo-encephalitis from other etiologies (control group. In 7 patients from the test group antigenic DNA sequences of Mycobacterium tuberculosis were found in CSF by PCR; positive results were not registered in the control group.

45 CFR 150.217 - Preliminary determination.

Science.gov (United States)

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Preliminary determination. 150.217 Section 150.217... Are Failing To Substantially Enforce HIPAA Requirements § 150.217 Preliminary determination. If, at... designees). (b) Notifies the State of CMS's preliminary determination that the State has failed to...

Regression models of reactor diagnostic signals

International Nuclear Information System (INIS)

Vavrin, J.

1989-01-01

The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)

Gender effects in gaming research: a case for regression residuals?

Science.gov (United States)

Pfister, Roland

2011-10-01

Numerous recent studies have examined the impact of video gaming on various dependent variables, including the players' affective reactions, positive as well as detrimental cognitive effects, and real-world aggression. These target variables are typically analyzed as a function of game characteristics and player attributes-especially gender. However, findings on the uneven distribution of gaming experience between males and females, on the one hand, and the effect of gaming experience on several target variables, on the other hand, point at a possible confound when gaming experiments are analyzed with a standard analysis of variance. This study uses simulated data to exemplify analysis of regression residuals as a potentially beneficial data analysis strategy for such datasets. As the actual impact of gaming experience on each of the various dependent variables differs, the ultimate benefits of analysis of regression residuals entirely depend on the research question, but it offers a powerful statistical approach to video game research whenever gaming experience is a confounding factor.

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

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

Effects of methylphenidate during emotional processing in amphetamine users: preliminary findings.

Science.gov (United States)

Bottelier, M A; Schouw, M L J; de Ruiter, M B; Ruhe, H G; Lindauer, R J L; Reneman, L

2015-12-01

D-amphetamine (dAMPH) and methylphenidate (MPH) are stimulants used in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). Preclinical studies have shown that in healthy animals, dAMPH induces dopamine (DA) dysfunction, as evidenced for instance by loss of DA levels and its transporters. It has also been suggested that DA plays an important role in emotional processing, and that altered DA-ergic intervention may modulate amygdala function. To explore the role of the DA system in emotional processing we examined emotional processing using functional magnetic resonance imaging (fMRI) in eight male recreational users of dAMPH and eight male healthy controls. We compared brain activation between both groups during an emotional face-processing task with and without an oral MPH challenge. All subjects were abstinent for at least 2 weeks during the baseline scan. The second scan was performed on the same day 1½ hours after receiving an oral dose of 35 mg MPH. A significant Valence*Group interaction (p = .037) indicated amygdala hyperreactivity to fearful facial expressions in dAMPH users that was robust against adjustment for age (p = .015). Furthermore, duration of amphetamine use in years was positively correlated with amygdala reactivity in dAMPH users (r = .76; p = .029). These exploratory findings are in line with previous findings suggesting that DA plays a role in emotional processing.

On Solving Lq-Penalized Regressions

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Tracy Zhou Wu

2007-01-01

Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.

Exploratory shaft facility preliminary designs - Permian Basin

International Nuclear Information System (INIS)

1983-09-01

The purpose of the Preliminary Design Report, Permian Basin, is to provide a description of the preliminary design for an Exploratory Shaft Facility in the Permian Basin, Texas. This issue of the report describes the preliminary design for constructing the exploratory shaft using the Large Hole Drilling method of construction and outlines the preliminary design and estimates of probable construction cost. The Preliminary Design Report is prepared to complement and summarize other documents that comprise the design at the preliminary stage of completion, December 1982. Other design documents include drawings, cost estimates and schedules. The preliminary design drawing package, which includes the construction schedule drawing, depicts the descriptions in this report. For reference, a list of the drawing titles and corresponding numbers are included in the Appendix. The report is divided into three principal sections: Design Basis, Facility Description, and Construction Cost Estimate. 30 references, 13 tables

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study.

Science.gov (United States)

Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y

2008-02-18

The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

Testing Heteroscedasticity in Robust Regression

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2011-01-01

Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf

Spontaneous regression of a congenital melanocytic nevus

Directory of Open Access Journals (Sweden)

Amiya Kumar Nath

2011-01-01

Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.

Sanctions as a tactic used in partner conflicts: theoretical, operational, and preliminary findings.

Science.gov (United States)

Winstok, Zeev; Smadar-Dror, Ronit

2015-07-01

Partner sanction in this study is a form/tactic of violence, much like verbal and physical violence, which partners use toward each other during their conflicts. The partner sanction embodies a temporary deprivation of a mutually agreed-on right. The purpose of this study is to develop a theoretical and operational framework of sanctions partners use. The study sampled 74 heterosexual couples from the general population (148 male and female participants). The findings support the validity and reliability of the sanction measurement. Furthermore, findings indicate that the use of sanctions between partners is highly prevalent among men and women in the general population; that the more one partner uses sanctions, the more the other partner uses it; and that sanctions are strongly associated with other violent tactics partners use in their conflict (i.e., verbal and physical). Theoretical and empirical implications of the theoretical framework and the findings are discussed, including the role of sanctions in partner conflicts that escalate to severe forms of violence. © The Author(s) 2014.

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

Science.gov (United States)

Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

Reliability and preliminary evidence of validity of a Farsi version of the depression anxiety stress scales.

Science.gov (United States)

Bayani, Ali Asghar

2010-08-01

The internal consistency, test-retest reliability, and construct validity of the Farsi version of the Depression Anxiety Stress Scales were examined, with a sample of 306 undergraduate students (123 men, 183 women) ranging from 18 to 51 years of age (M age = 25.4, SD = 6.1). Participants completed the Satisfaction with Life Scale, Rosenberg Self-esteem Scale, and the Depression Anxiety Stress Scales. The findings confirmed the preliminary reliabilities and preliminary construct validity of the Farsi translation of the Depression Anxiety Stress Scales.

Helminthiases in Montes Claros. Preliminary survey

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Rina Girard Kaminsky

1976-04-01

Full Text Available A preliminary survey was conducted for the presence of helminths in the city of Montes Claros, M. G., Brazil. Three groups of persons were examined by the direct smear, Kato thick film and MIFC techniques; one group by direct smear and Kato only. General findings were: a high prevalence of hookworm, followed by ascariasis, S. mansoni, S. stercoralis and very light infections with T. trichiurá. E. vermicularis and H. nana were ranking parasites at an orphanage, with some hookworm and S. mansoni infections as well. At a pig slaughter house, the dominant parasites were hookworm and S. mansoni. Pig cysticercosis was an incidental finding worth mentioning for the health hazard it represents for humans as well as an economic loss. From the comparative results between the Kato and the MIF the former proved itself again as a more sensitive and reliable concentration method for helminth eggs, of low cost and easy performance.

Regression Analysis by Example. 5th Edition

Science.gov (United States)

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

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 and thoroughly…

Gaussian process regression analysis for functional data

CERN Document Server

Shi, Jian Qing

2011-01-01

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

Is past life regression therapy ethical?

Science.gov (United States)

Andrade, Gabriel

2017-01-01

Past life regression therapy is used by some physicians in cases with some mental diseases. Anxiety disorders, mood disorders, and gender dysphoria have all been treated using life regression therapy by some doctors on the assumption that they reflect problems in past lives. Although it is not supported by psychiatric associations, few medical associations have actually condemned it as unethical. In this article, I argue that past life regression therapy is unethical for two basic reasons. First, it is not evidence-based. Past life regression is based on the reincarnation hypothesis, but this hypothesis is not supported by evidence, and in fact, it faces some insurmountable conceptual problems. If patients are not fully informed about these problems, they cannot provide an informed consent, and hence, the principle of autonomy is violated. Second, past life regression therapy has the great risk of implanting false memories in patients, and thus, causing significant harm. This is a violation of the principle of non-malfeasance, which is surely the most important principle in medical ethics.

Weighted SGD for ℓp Regression with Randomized Preconditioning*

Science.gov (United States)

Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W.

2018-01-01

prediction norm in 𝒪(log n·nnz(A)+poly(d) log(1/ε)/ε) time. We show that for unconstrained ℓ2 regression, this complexity is comparable to that of RLA and is asymptotically better over several state-of-the-art solvers in the regime where the desired accuracy ε, high dimension n and low dimension d satisfy d ≥ 1/ε and n ≥ d2/ε. We also provide lower bounds on the coreset complexity for more general regression problems, indicating that still new ideas will be needed to extend similar RLA preconditioning ideas to weighted SGD algorithms for more general regression problems. Finally, the effectiveness of such algorithms is illustrated numerically on both synthetic and real datasets, and the results are consistent with our theoretical findings and demonstrate that pwSGD converges to a medium-precision solution, e.g., ε = 10−3, more quickly. PMID:29782626

Forecast Model of Urban Stagnant Water Based on Logistic Regression

Directory of Open Access Journals (Sweden)

Liu Pan

2017-01-01

Full Text Available With the development of information technology, the construction of water resource system has been gradually carried out. In the background of big data, the work of water information needs to carry out the process of quantitative to qualitative change. Analyzing the correlation of data and exploring the deep value of data which are the key of water information’s research. On the basis of the research on the water big data and the traditional data warehouse architecture, we try to find out the connection of different data source. According to the temporal and spatial correlation of stagnant water and rainfall, we use spatial interpolation to integrate data of stagnant water and rainfall which are from different data source and different sensors, then use logistic regression to find out the relationship between them.

A robust ridge regression approach in the presence of both multicollinearity and outliers in the data

Science.gov (United States)

Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah

2017-08-01

Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

Regression Models for Market-Shares

DEFF Research Database (Denmark)

Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue

2005-01-01

On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....

A comparison of gallium-67 citrate scintigraphy and indium-111 labelled leukocyte imaging for the diagnosis of prosthetic joint infection. Preliminary results

International Nuclear Information System (INIS)

McKillop, J.H.; Cuthbert, G.F.; Gray, H.W.; McKay, Iain; Sturrock, R.D.

1982-01-01

Preliminary experience in comparing Gallium-67 imaging in patients with a painful prosthetic joint to the findings on Indium-111 labelled leukocyte imaging is reported. In the small series of patients so far studied, no clear advantage has emerged for either Gallium-67 or Indium-111 leukocyte imaging in terms of sensitivity or specificity for joint prosthesis infection. Should a larger group confirm the preliminary findings, Gallium-67 imaging may be preferable to Indium-111 leukocyte imaging in the patient with the painful joint prosthesis, in view of the greater simplicity of the former technique

Multivariate Multiple Regression Models for a Big Data-Empowered SON Framework in Mobile Wireless Networks

Directory of Open Access Journals (Sweden)

Yoonsu Shin

2016-01-01

Full Text Available In the 5G era, the operational cost of mobile wireless networks will significantly increase. Further, massive network capacity and zero latency will be needed because everything will be connected to mobile networks. Thus, self-organizing networks (SON are needed, which expedite automatic operation of mobile wireless networks, but have challenges to satisfy the 5G requirements. Therefore, researchers have proposed a framework to empower SON using big data. The recent framework of a big data-empowered SON analyzes the relationship between key performance indicators (KPIs and related network parameters (NPs using machine-learning tools, and it develops regression models using a Gaussian process with those parameters. The problem, however, is that the methods of finding the NPs related to the KPIs differ individually. Moreover, the Gaussian process regression model cannot determine the relationship between a KPI and its various related NPs. In this paper, to solve these problems, we proposed multivariate multiple regression models to determine the relationship between various KPIs and NPs. If we assume one KPI and multiple NPs as one set, the proposed models help us process multiple sets at one time. Also, we can find out whether some KPIs are conflicting or not. We implement the proposed models using MapReduce.

Detection of epistatic effects with logic regression and a classical linear regression model.

Science.gov (United States)

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

Science.gov (United States)

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

Corporate Social Disclosures in Southeast Asia: A Preliminary Study

Directory of Open Access Journals (Sweden)

Juniati Gunawan

2012-12-01

Full Text Available The issue of Corporate Social Disclosure (CSD has been growing remarkably both in business and academic world.  Inevitably, this topic is also exposed in Southeast Asia, a big region that plays important role in global economic issue. Applying a content analysis method, this paper aims to provide preliminary findings in CSD practices throughout the companies‟ annual reports in 2007 and 2008 for countries located in Southeast Asia.  Samples were selected for listed and unlisted various type of industries, based on the information availability internet searching. The sample collection and the subjectivity during the content analysis process are the limitations in conducting this study. In general, the results show that „human resources‟ are the main information disclosed, while in contrast, „energy‟ is the main least issue disclosed in the annual reports.  However, the findings need to be interpreted with considerations since there are limited in samples. Basically, the outcomes support the major prior studies and enhancing the discussion of CSD conducting in developing countries, while at the same time describing some countries which obtained very limited in exposures. To respond the vast increasing issues of CSD practice, this preliminary study has provided a basis to see the role of every country in CSR reporting and how they could support the sustainability development globally.

Corporate Social Responsibility and Financial Performance: A Two Least Regression Approach

Directory of Open Access Journals (Sweden)

Alexander Olawumi Dabor

2017-12-01

Full Text Available The objective of this study is to investigate the casuality between corporate social responsibility and firm financial performance. The study employed two least square regression approaches. Fifty-two firms were selected using the scientific method. The findings revealed that corporate social responsibility and firm performance in manufacturing sector are mutually related at 5%. The study recommended that management of manufacturing companies in Nigeria should expend on CSR to boost profitability and corporate image.

Auxiliary variables in multiple imputation in regression with missing X: a warning against including too many in small sample research

Directory of Open Access Journals (Sweden)

Hardt Jochen

2012-12-01

Full Text Available Abstract Background Multiple imputation is becoming increasingly popular. Theoretical considerations as well as simulation studies have shown that the inclusion of auxiliary variables is generally of benefit. Methods A simulation study of a linear regression with a response Y and two predictors X1 and X2 was performed on data with n = 50, 100 and 200 using complete cases or multiple imputation with 0, 10, 20, 40 and 80 auxiliary variables. Mechanisms of missingness were either 100% MCAR or 50% MAR + 50% MCAR. Auxiliary variables had low (r=.10 vs. moderate correlations (r=.50 with X’s and Y. Results The inclusion of auxiliary variables can improve a multiple imputation model. However, inclusion of too many variables leads to downward bias of regression coefficients and decreases precision. When the correlations are low, inclusion of auxiliary variables is not useful. Conclusion More research on auxiliary variables in multiple imputation should be performed. A preliminary rule of thumb could be that the ratio of variables to cases with complete data should not go below 1 : 3.

Online gaming dependency: a preliminary study in China.

Science.gov (United States)

Peng, Wei; Liu, Ming

2010-06-01

Based on theories and previous studies on problematic Internet use, we propose a model to better understand the contributors to and consequences of online gaming dependency. A preliminary study was conducted through a survey of online gamers in China. The results of path analysis found that maladaptive cognitions, shyness, and depression are positively related to online gaming dependency. Online gaming dependency was also positively related to different types of negative life outcomes. The findings of this study have implications for the prevention and treatment of addictive online gaming.

Preliminary Multi-Variable Cost Model for Space Telescopes

Science.gov (United States)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. This paper reviews the methodology used to develop space telescope cost models; summarizes recently published single variable models; and presents preliminary results for two and three variable cost models. Some of the findings are that increasing mass reduces cost; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and technology development as a function of time reduces cost at the rate of 50% per 17 years.

Development of an integrated optical coherence tomography-gas nozzle system for surgical laser ablation applications: preliminary findings of in situ spinal cord deformation due to gas flow effects.

Science.gov (United States)

Wong, Ronnie; Jivraj, Jamil; Vuong, Barry; Ramjist, Joel; Dinn, Nicole A; Sun, Cuiru; Huang, Yize; Smith, James A; Yang, Victor X D

2015-01-01

Gas assisted laser machining of materials is a common practice in the manufacturing industry. Advantages in using gas assistance include reducing the likelihood of flare-ups in flammable materials and clearing away ablated material in the cutting path. Current surgical procedures and research do not take advantage of this and in the case for resecting osseous tissue, gas assisted ablation can help minimize charring and clear away debris from the surgical site. In the context of neurosurgery, the objective is to cut through osseous tissue without damaging the underlying neural structures. Different inert gas flow rates used in laser machining could cause deformations in compliant materials. Complications may arise during surgical procedures if the dura and spinal cord are damaged by these deformations. We present preliminary spinal deformation findings for various gas flow rates by using optical coherence tomography to measure the depression depth at the site of gas delivery.

Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Study on Thermal Degradation Characteristics and Regression Rate Measurement of Paraffin-Based Fuel

Directory of Open Access Journals (Sweden)

Songqi Hu

2015-09-01

Full Text Available Paraffin fuel has been found to have a regression rate that is higher than conventional HTPB (hydroxyl-terminated polybutadiene fuel and, thus, presents itself as an ideal energy source for a hybrid rocket engine. The energy characteristics of paraffin-based fuel and HTPB fuel have been calculated by the method of minimum free energy. The thermal degradation characteristics were measured for paraffin, pretreated paraffin, HTPB and paraffin-based fuel in different working conditions by the using differential scanning calorimetry (DSC and a thermogravimetric analyzer (TGA. The regression rates of paraffin-based fuel and HTPB fuel were tested by a rectangular solid-gas hybrid engine. The research findings showed that: the specific impulse of paraffin-based fuel is almost the same as that of HTPB fuel; the decomposition temperature of pretreated paraffin is higher than that of the unprocessed paraffin, but lower than that of HTPB; with the increase of paraffin, the initial reaction exothermic peak of paraffin-based fuel is reached in advance, and the initial reaction heat release also increases; the regression rate of paraffin-based fuel is higher than the common HTPB fuel under the same conditions; with the increase of oxidizer mass flow rate, the regression rate of solid fuel increases accordingly for the same fuel formulation.

Regression analysis using dependent Polya trees.

Science.gov (United States)

Schörgendorfer, Angela; Branscum, Adam J

2013-11-30

Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

Regression analysis of radiological parameters in nuclear power plants

International Nuclear Information System (INIS)

Bhargava, Pradeep; Verma, R.K.; Joshi, M.L.

2003-01-01

Indian Pressurized Heavy Water Reactors (PHWRs) have now attained maturity in their operations. Indian PHWR operation started in the year 1972. At present there are 12 operating PHWRs collectively producing nearly 2400 MWe. Sufficient radiological data are available for analysis to draw inferences which may be utilised for better understanding of radiological parameters influencing the collective internal dose. Tritium is the main contributor to the occupational internal dose originating in PHWRs. An attempt has been made to establish the relationship between radiological parameters, which may be useful to draw inferences about the internal dose. Regression analysis have been done to find out the relationship, if it exist, among the following variables: A. Specific tritium activity of heavy water (Moderator and PHT) and tritium concentration in air at various work locations. B. Internal collective occupational dose and tritium release to environment through air route. C. Specific tritium activity of heavy water (Moderator and PHT) and collective internal occupational dose. For this purpose multivariate regression analysis has been carried out. D. Tritium concentration in air at various work location and tritium release to environment through air route. For this purpose multivariate regression analysis has been carried out. This analysis reveals that collective internal dose has got very good correlation with the tritium activity release to the environment through air route. Whereas no correlation has been found between specific tritium activity in the heavy water systems and collective internal occupational dose. The good correlation has been found in case D and F test reveals that it is not by chance. (author)

Colonisation trends of the invasive plant, Impatiens glandulifera, along river corridors: some preliminary findings

Science.gov (United States)

Greenwood, Phil; Kuhn, Brigitte; Kuhn, Nikolaus

2016-04-01

-density, finer grain-size characteristics, and possibly higher total phosphorous (TP) content, when compared against soils from nearby uncontaminated areas. Approximately 250 pairs of (contaminated and uncontaminated) soil samples were obtained from nine different sub-catchments located in four different European countries; namely, France, Germany, Switzerland and the UK. Sample pairs were sub-divided into contaminated & uncontaminated soils and each variable was subjected to a pair-wise statistical test; firstly for all catchments combined, and then on a catchment-by-catchment basis, to determine whether differences were significant. In addition to the above analyses, further evidence of spatial and topographic colonisation tendencies was sought from digital imagery captured using a remotely-controlled drone (quadcopter) flown along a ca. 1.0 km section of contaminated river corridor. Images were georeferenced, displayed together in a Geographic Information System (GIS) and used to construct a 3-dimensional digital elevation model (DEM). The DEM was interrogated to determine the presence / absence of colonisation trends (i.e. a tendency to colonise low-lying areas). This communication reports preliminary findings from this ongoing work and discusses key implications and possible future directions.

MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY

OpenAIRE

Chayalakshmi C.L

2018-01-01

MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY ABSTRACT Calculation of boiler efficiency is essential if its parameters need to be controlled for either maintaining or enhancing its efficiency. But determination of boiler efficiency using conventional method is time consuming and very expensive. Hence, it is not recommended to find boiler efficiency frequently. The work presented in this paper deals with establishing the statistical mo...

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...

Applied Regression Modeling A Business Approach

CERN Document Server

Pardoe, Iain

2012-01-01

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

Traffics and wildlife: A preliminary study on road-kill

OpenAIRE

Rustiati, Elly Lestari

2012-01-01

This paper presents the preliminary finding on road kill survey by direct observations onthe high ways. The road-kills recorded of small wildlife, including medium size-mammal (2.50%, n =1), birds (5.00%, n = 2) and small mammals (92.50%, n = 37). The small mammals include the mostcommon mammals in the areas, squirrels, raccoons, skunks and woodchuck. Of mammals, squirrels(35.00%) were the highest recorded, followed by woodchucks (25.00%), mice/shrew (17.50%),raccoons (10.00%), skunk (5.00%) ...

Multiple Linear Regression Analysis of Factors Affecting Real Property Price Index From Case Study Research In Istanbul/Turkey

Science.gov (United States)

Denli, H. H.; Koc, Z.

2015-12-01

Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.

Regression of environmental noise in LIGO data

International Nuclear Information System (INIS)

Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G

2015-01-01

We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

Science.gov (United States)

van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

2016-11-24

Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Forecasting with Dynamic Regression Models

CERN Document Server

Pankratz, Alan

2012-01-01

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

Regression analysis: An evaluation of the inuences behindthe pricing of beer

OpenAIRE

Eriksson, Sara; Häggmark, Jonas

2017-01-01

This bachelor thesis in applied mathematics is an analysis of which factors affect the pricing of beer at the Swedish market. A multiple linear regression model is created with the statistical programming language R through a study of the influences for several explanatory variables. For example these variables include country of origin, beer style, volume sold and a Bayesian weighted mean rating from RateBeer, a popular website for beer enthusiasts. The main goal of the project is to find si...

Detecting nonsense for Chinese comments based on logistic regression

Science.gov (United States)

Zhuolin, Ren; Guang, Chen; Shu, Chen

2016-07-01

To understand cyber citizens' opinion accurately from Chinese news comments, the clear definition on nonsense is present, and a detection model based on logistic regression (LR) is proposed. The detection of nonsense can be treated as a binary-classification problem. Besides of traditional lexical features, we propose three kinds of features in terms of emotion, structure and relevance. By these features, we train an LR model and demonstrate its effect in understanding Chinese news comments. We find that each of proposed features can significantly promote the result. In our experiments, we achieve a prediction accuracy of 84.3% which improves the baseline 77.3% by 7%.

Substitution elasticities between GHG-polluting and nonpolluting inputs in agricultural production: A meta-regression

International Nuclear Information System (INIS)

Liu, Boying; Richard Shumway, C.

2016-01-01

This paper reports meta-regressions of substitution elasticities between greenhouse gas (GHG) polluting and nonpolluting inputs in agricultural production, which is the main feedstock source for biofuel in the U.S. We treat energy, fertilizer, and manure collectively as the “polluting input” and labor, land, and capital as nonpolluting inputs. We estimate meta-regressions for samples of Morishima substitution elasticities for labor, land, and capital vs. the polluting input. Much of the heterogeneity of Morishima elasticities can be explained by type of primal or dual function, functional form, type and observational level of data, input categories, number of outputs, type of output, time period, and country categories. Each estimated long-run elasticity for the reference case, which is most relevant for assessing GHG emissions through life-cycle analysis, is greater than 1.0 and significantly different from zero. Most predicted long-run elasticities remain significantly different from zero at the data means. These findings imply that life-cycle analysis based on fixed proportion production functions could provide grossly inaccurate measures of GHG of biofuel. - Highlights: • This paper reports meta-regressions of substitution elasticities between greenhouse-gas (GHG) polluting and nonpolluting inputs in agricultural production, which is the main feedstock source for biofuel in the U.S. • We estimate meta-regressions for samples of Morishima substitution elasticities for labor, land, and capital vs. the polluting input based on 65 primary studies. • We found that each estimated long-run elasticity for the reference case, which is most relevant for assessing GHG emissions through life-cycle analysis, is greater than 1.0 and significantly different from zero. Most predicted long-run elasticities remain significantly different from zero at the data means. • These findings imply that life-cycle analysis based on fixed proportion production functions could

Preliminary Study of Perception and Consumer Behaviour Towards Energy Saving for Household Appliances: A Case of Makassar

Science.gov (United States)

Syam Akil, Yusri; Mangngenre, Saiful; Mawar, Sri; Amar, Kifayah

2018-03-01

Electricity load has tendency to increase over the time. Therefore, efforts to maintain a balance between electricity supply and demand such as increasing energy saving related to the use of home electricity appliances are urgently needed. In general, one of the household appliances which consumes relatively high electricity energy is refrigerator. The purpose of this study is to analyze residential consumers perceptions and their behaviours about electricity energy saving in relation to the usage of household appliances in Makassar, Indonesia particularly for refrigerator. Moreover, typical relationship between perceptions and consumers behaviours is also analyzed by composed two regression models, namely model for usage behaviour (UREFm model) and model for habitual behaviour (HREFm model) by using general perception, specific perception, and external factors as explanation variables. To collect data, a questionnaire was designed for survey which involved 40 respondents as a preliminary study and then statistical tests including regression analysis were applied to analyze usable data. The target of respondent was an owner of a house in Makassar with installed power capacity at least 900 VA. Reliability test shown that all items in the developed questionnaire can be used for main survey as obtained Cronbach’s alpha values were above 0.6. Evaluation for consumers perceptions on energy saving in relation to demographic aspect using mean and Standard Deviation values indicated some significant differences. Other results regarding regression analysis shown that both composed models were well validated and had quite good fitness degree with adjusted R-squared values around 49.31% for UREFm model and 80.90% for HREFm model. Among considered variables, specific perception, and external factors were found have significant influence to the usage and habitual behaviours of consumers as confirmed by their p-values in each model below 0.05. Findings of this research can be

A Comparative Analysis of the ADOS-G and ADOS-2 Algorithms: Preliminary Findings.

Science.gov (United States)

Dorlack, Taylor P; Myers, Orrin B; Kodituwakku, Piyadasa W

2018-06-01

The Autism Diagnostic Observation Schedule (ADOS) is a widely utilized observational assessment tool for diagnosis of autism spectrum disorders. The original ADOS was succeeded by the ADOS-G with noted improvements. More recently, the ADOS-2 was introduced to further increase its diagnostic accuracy. Studies examining the validity of the ADOS have produced mixed findings, and pooled relationship trends between the algorithm versions are yet to be analyzed. The current review seeks to compare the relative merits of the ADOS-G and ADOS-2 algorithms, Modules 1-3. Eight studies met inclusion criteria for the review, and six were selected for paired comparisons of the sensitivity and specificity of the ADOS. Results indicate several contradictory findings, underscoring the importance of further study.

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.

Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

International Nuclear Information System (INIS)

Kavaklioglu, Kadir

2011-01-01

Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)

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.

Gibrat’s law and quantile regressions

DEFF Research Database (Denmark)

Distante, Roberta; Petrella, Ivan; Santoro, Emiliano

2017-01-01

The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...

ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES

NARCIS (Netherlands)

RUSCHENDORF, L; DEVALK, [No Value

We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive

A preliminary study on the CT finding in SARS following hospital discharge

International Nuclear Information System (INIS)

Zhang Lieguang; Liu Jinxing; Chen Bihua; Jiang Songfeng

2004-01-01

Objective: To study the CT finding of chest in patient with SARS following hospital discharge. Methods: Thirty-six patients (11 men, 25 women; age range, 20-73 years; mean age, 39 years) with confirmed SARS underwent follow-up spiral CT. The scans were obtained on average 187 days (range from 152 days to 225 days) after onset of symptoms. Patients were assigned to group 1 (with heavy SARS, n=19) and group 2 (with common SARS, n=17) for analysis. The chest X-ray films of the 36 patients in fastigium of film were retrospectively reviewed. Results: 58.33% (21 of 36) cases are normal on the CT of thorax. In group 1 42.11% (8 of 19) cases and in group 2 76.47%(13 of 17) cases. In group 1: 31.58%(6 of 19) cases present diffuse ground-glass opacification, 21.05% (4 of 19) cases present multi-patch ground-glass opacification, 5.26% (1 of 19) cases present local ground-glass opacification in single lobar, 31.58% (6 of 19) cases present intralobular interstitial thickening and/or interlobular septal thickening, 5.26% (1 of 19) present subpleural lines, 5.26% (1 of 19) present honeycombing, 5.26% (1 of 19) cases present bullae; In group 2: 11.76% (2 of 17) cases present local ground-glass opacification, 11.76%(2 of 17) cases present intralobular interstitial thickening and/or interlobular septal thickening, 5.88%(1 of 17) cases present organized pneumonia. In group 1, 73.68% (14/19) cases in fastigium of film present large areas of lung consolidation and diffuse ground-glass opacification. Conclusion: Most of the healing SARS cases after certain time are normal on the CT finding of thorax. Part of them remain manifests such as ground-glass opacification, intralobular interstitial thickening and/or interlobular septal thickening, subpleural lines, honeycombing, traction bronchiectasis, organized pneumonia and bullae. They relate to severeness of the lesion of the lung in fastigium of film. Such finding can last for long time and probably fibrosis can be developed. (authors)

Introduction to the use of regression models in epidemiology.

Science.gov (United States)

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

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....

Producing The New Regressive Left

DEFF Research Database (Denmark)

Crone, Christine

members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...

28 CFR 2.48 - Revocation: Preliminary interview.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Revocation: Preliminary interview. 2.48....48 Revocation: Preliminary interview. (a) Interviewing officer. A parolee who is retaken on a warrant issued by a Commissioner shall be given a preliminary interview by an official designated by the Regional...

Using Financial Ratios to Select Companies for Tax Auditing: A Preliminary Study

Science.gov (United States)

Marghescu, Dorina; Kallio, Minna; Back, Barbro

Tax auditing procedures include an investigation of the accounting records of a company and of other sources of information in order to assess whether the taxation has been based on correct and complete information. When there are found discrepancies between the accounting information and the real situation, the taxation should be corrected so that the eventual tax defaults are assessed and debited. The paper analyzes to what extent the financial performance of a company can be used as an indicator of tax defaults. We focus on one type of tax, namely employer's contribution, and four financial ratios. We evaluate the model in a study of Finnish companies by using a binomial logistic regression analysis. The study is exploratory and at a preliminary stage.

Mixture of Regression Models with Single-Index

OpenAIRE

Xiang, Sijia; Yao, Weixin

2016-01-01

In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...

Local bilinear multiple-output quantile/depth regression

Czech Academy of Sciences Publication Activity Database

Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav

2015-01-01

Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf

Do clinical and translational science graduate students understand linear regression? Development and early validation of the REGRESS quiz.

Science.gov (United States)

Enders, Felicity

2013-12-01

Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.

The MIDAS Touch: Mixed Data Sampling Regression Models

OpenAIRE

Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen

2004-01-01

We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and �nance.

Suppression Situations in Multiple Linear Regression

Science.gov (United States)

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Significance testing in ridge regression for genetic data

Directory of Open Access Journals (Sweden)

De Iorio Maria

2011-09-01

Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.

Regression calibration with more surrogates than mismeasured variables

KAUST Repository

Kipnis, Victor

2012-06-29

In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

Regression calibration with more surrogates than mismeasured variables

KAUST Repository

Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.

2012-01-01

In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

International Nuclear Information System (INIS)

Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard

2009-01-01

Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model.

Science.gov (United States)

Islam, Mohammad Mafijul; Alam, Morshed; Tariquzaman, Md; Kabir, Mohammad Alamgir; Pervin, Rokhsona; Begum, Munni; Khan, Md Mobarak Hossain

2013-01-08

Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance variable namely mother's education, father's education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh.

Few crystal balls are crystal clear : eyeballing regression

International Nuclear Information System (INIS)

Wittebrood, R.T.

1998-01-01

The theory of regression and statistical analysis as it applies to reservoir analysis was discussed. It was argued that regression lines are not always the final truth. It was suggested that regression lines and eyeballed lines are often equally accurate. The many conditions that must be fulfilled to calculate a proper regression were discussed. Mentioned among these conditions were the distribution of the data, hidden variables, knowledge of how the data was obtained, the need for causal correlation of the variables, and knowledge of the manner in which the regression results are going to be used. 1 tab., 13 figs

Chemical Analysis of the Moon at the Surveyor VI Landing Site: Preliminary Results.

Science.gov (United States)

Turkevich, A L; Patterson, J H; Franzgrote, E J

1968-06-07

The alpha-scattering experiment aboard soft-landing Surveyor VI has provided a chemical analysis of the surface of the moon in Sinus Medii. The preliminary results indicate that, within experimental errors, the composition is the same as that found by Surveyor V in Mare Tranquillitatis. This finding suggests that large portions of the lunar maria resemble basalt in composition.

Preliminary study of soil permeability properties using principal component analysis

Science.gov (United States)

Yulianti, M.; Sudriani, Y.; Rustini, H. A.

2018-02-01

Soil permeability measurement is undoubtedly important in carrying out soil-water research such as rainfall-runoff modelling, irrigation water distribution systems, etc. It is also known that acquiring reliable soil permeability data is rather laborious, time-consuming, and costly. Therefore, it is desirable to develop the prediction model. Several studies of empirical equations for predicting permeability have been undertaken by many researchers. These studies derived the models from areas which soil characteristics are different from Indonesian soil, which suggest a possibility that these permeability models are site-specific. The purpose of this study is to identify which soil parameters correspond strongly to soil permeability and propose a preliminary model for permeability prediction. Principal component analysis (PCA) was applied to 16 parameters analysed from 37 sites consist of 91 samples obtained from Batanghari Watershed. Findings indicated five variables that have strong correlation with soil permeability, and we recommend a preliminary permeability model, which is potential for further development.

Regression methods for medical research

CERN Document Server

Tai, Bee Choo

2013-01-01

Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the

Should metacognition be measured by logistic regression?

Science.gov (United States)

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

On Preliminary Test Estimator for Median

OpenAIRE

Okazaki, Takeo; 岡崎, 威生

1990-01-01

The purpose of the present paper is to discuss about estimation of median with a preliminary test. Two procedures are presented, one uses Median test and the other uses Wilcoxon two-sample test for the preliminary test. Sections 3 and 4 give mathematical formulations of such properties, including mean square errors with one specified case. Section 5 discusses their optimal significance levels of the preliminary test and proposes their numerical values by Monte Carlo method. In addition to mea...

National Data Center Preparedness Exercise 2015 (NPE 2015): MY-NDC Preliminary Analysis Result

International Nuclear Information System (INIS)

Faisal Izwan Abdul Rashid; Muhammed Zulfakar Zolkaffly

2016-01-01

Malaysia has established the CTBT National Data Centre (MY-NDC) in December 2005. MY-NDC is tasked to perform Comprehensive Nuclear-Test-Ban-Treaty (CTBT) data management as well as provide information for Treaty related events to Nuclear Malaysia as CTBT National Authority. In 2015, MY-NDC has participated in the National Data Centre Preparedness Exercise 2015 (NPE 2015). This paper aims at presenting MY-NDC preliminary analysis result of NPE 2015. In NPE 2015, MY-NDC has performed five different analyses, namely, radionuclide, atmospheric transport modelling (ATM), data fusion, seismic analysis and site forensics. The preliminary findings show the hypothetical scenario in NPE 2015 most probably is an uncontained event resulted high release of radionuclide to the air. (author)

Sample size calculation to externally validate scoring systems based on logistic regression models.

Directory of Open Access Journals (Sweden)

Antonio Palazón-Bru

Full Text Available A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence. Scoring systems based on binary logistic regression models are a specific type of predictive model.The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.

A Branch-and-Price approach to find optimal decision trees

NARCIS (Netherlands)

Firat, M.; Crognier, Guillaume; Gabor, Adriana; Zhang, Y.

2018-01-01

In Artificial Intelligence (AI) field, decision trees have gained certain importance due to their effectiveness in solving classification and regression problems. Recently, in the literature we see finding optimal decision trees are formulated as Mixed Integer Linear Programming (MILP) models. This

BOX-COX REGRESSION METHOD IN TIME SCALING

Directory of Open Access Journals (Sweden)

ATİLLA GÖKTAŞ

2013-06-01

Full Text Available Box-Cox regression method with λj, for j = 1, 2, ..., k, power transformation can be used when dependent variable and error term of the linear regression model do not satisfy the continuity and normality assumptions. The situation obtaining the smallest mean square error  when optimum power λj, transformation for j = 1, 2, ..., k, of Y has been discussed. Box-Cox regression method is especially appropriate to adjust existence skewness or heteroscedasticity of error terms for a nonlinear functional relationship between dependent and explanatory variables. In this study, the advantage and disadvantage use of Box-Cox regression method have been discussed in differentiation and differantial analysis of time scale concept.

Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression

Directory of Open Access Journals (Sweden)

Stephen M. Akandwanaho

2014-01-01

Full Text Available This paper solves the dynamic traveling salesman problem (DTSP using dynamic Gaussian Process Regression (DGPR method. The problem of varying correlation tour is alleviated by the nonstationary covariance function interleaved with DGPR to generate a predictive distribution for DTSP tour. This approach is conjoined with Nearest Neighbor (NN method and the iterated local search to track dynamic optima. Experimental results were obtained on DTSP instances. The comparisons were performed with Genetic Algorithm and Simulated Annealing. The proposed approach demonstrates superiority in finding good traveling salesman problem (TSP tour and less computational time in nonstationary conditions.

Gaussian Process Regression Model in Spatial Logistic Regression

Science.gov (United States)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Regression Analysis and the Sociological Imagination

Science.gov (United States)

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

Increased error rates in preliminary reports issued by radiology residents working more than 10 consecutive hours overnight.

Science.gov (United States)

Ruutiainen, Alexander T; Durand, Daniel J; Scanlon, Mary H; Itri, Jason N

2013-03-01

To determine if the rate of major discrepancies between resident preliminary reports and faculty final reports increases during the final hours of consecutive 12-hour overnight call shifts. Institutional review board exemption status was obtained for this study. All overnight radiology reports interpreted by residents on-call between January 2010 and June 2010 were reviewed by board-certified faculty and categorized as major discrepancies if they contained a change in interpretation with the potential to impact patient management or outcome. Initial determination of a major discrepancy was at the discretion of individual faculty radiologists based on this general definition. Studies categorized as major discrepancies were secondarily reviewed by the residency program director (M.H.S.) to ensure consistent application of the major discrepancy designation. Multiple variables associated with each report were collected and analyzed, including the time of preliminary interpretation, time into shift study was interpreted, volume of studies interpreted during each shift, day of the week, patient location (inpatient or emergency department), block of shift (2-hour blocks for 12-hour shifts), imaging modality, patient age and gender, resident identification, and faculty identification. Univariate risk factor analysis was performed to determine the optimal data format of each variable (ie, continuous versus categorical). A multivariate logistic regression model was then constructed to account for confounding between variables and identify independent risk factors for major discrepancies. We analyzed 8062 preliminary resident reports with 79 major discrepancies (1.0%). There was a statistically significant increase in major discrepancy rate during the final 2 hours of consecutive 12-hour call shifts. Multivariate analysis confirmed that interpretation during the last 2 hours of 12-hour call shifts (odds ratio (OR) 1.94, 95% confidence interval (CI) 1.18-3.21), cross

Preliminary results of a techno-economic assessment of CO2 capture-network configurations in the industry

NARCIS (Netherlands)

Berghout, N.A.; Kuramochi, T.; van den Broek, M.A.; Ramirez, C.A.; Faaij, A.P.C.

2013-01-01

This paper evaluated the techno economic performance of several CO2 capture-network configurations for a cluster of sixteen industrial plants in the Netherlands using bottom up analysis. Preliminary findings indicate that centralizing capture equipment instead of capture equipment at plant sites

A Preliminary Investigation into the Search Behaviour of Users in a Collection of Digitized Broadcast Audio

DEFF Research Database (Denmark)

Lund, Haakon; Skov, Mette; Larsen, Birger

2014-01-01

An increasing number of large digitized audio-visual collections within digital humanities have recently been made available for users. Often access to digitized audio-visual collections is hampered by little and inconsistent metadata. This paper presents the preliminary findings from a study of ...

A Preliminary Analysis of the Outcomes of Students Assisted by VET FEE-HELP: Summary

Science.gov (United States)

National Centre for Vocational Education Research (NCVER), 2015

2015-01-01

This summary highlights the key findings from the report "A preliminary analysis of the outcomes of students assisted by VET FEE-HELP". VET FEE-HELP is an income-contingent loan scheme that assists eligible students undertaking certain vocational education training (VET) courses with an approved provider by paying for all or part of…

Preliminary Findings in the Development of a Theoretical Framework for Investigating ICT Integration in Teacher Education

Directory of Open Access Journals (Sweden)

Suthagar Narasuman

2012-06-01

Full Text Available The following report is the result of a preliminary investigation in the development of a theoretical framework for investigating ICT integration, particularly in TESL (Teaching of English as a Second Language teacher training. The study is primarily an empirical effort to develop a theoretical framework for investigating ICT integration in TESL teacher training. In identifying the predictive variables for the framework, the researchers conducted an intensive review of the literature which included a review of various models used in studies on ICT integration. The contributing variables identified in the present study were age, gender, experience, ICT proficiency, attitude, access to ICT infrastructure, support services, and exposure to ICT professional development programmes. In developing the framework, the study sought to determine the extent to which the observed variability in ICT integration could be predicted by these factors. The sample comprised 266 respondents working at the faculty or English Language Unit in various teacher training institutions across the country. The study predominantly employed quantitative methods of data collection. Interview data was used to corroborate information derived from the survey data.

An Additive-Multiplicative Cox-Aalen Regression Model

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

Directory of Open Access Journals (Sweden)

Maarten van Smeden

2016-11-01

Full Text Available Abstract Background Ten events per variable (EPV is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared. Results The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’. We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Impact of clinical trial findings on Bell's palsy management in general practice in the UK 2001–2012: interrupted time series regression analysis

Science.gov (United States)

Morales, Daniel R; Donnan, Peter T; Daly, Fergus; Staa, Tjeerd Van; Sullivan, Frank M

2013-01-01

Objectives To measure the incidence of Bell's palsy and determine the impact of clinical trial findings on Bell's palsy management in the UK. Design Interrupted time series regression analysis and incidence measures. Setting General practices in the UK contributing to the Clinical Practice Research Datalink (CPRD). Participants Patients ≥16 years with a diagnosis of Bell's palsy between 2001 and 2012. Interventions (1) Publication of the 2004 Cochrane reviews of clinical trials on corticosteroids and antivirals for Bell's palsy, which made no clear recommendation on their use and (2) publication of the 2007 Scottish Bell's Palsy Study (SBPS), which made a clear recommendation that treatment with prednisolone alone improves chances for complete recovery. Main outcome measures Incidence of Bell's palsy per 100 000 person-years. Changes in the management of Bell's palsy with either prednisolone therapy, antiviral therapy, combination therapy (prednisolone with antiviral therapy) or untreated cases. Results During the 12-year period, 14 460 cases of Bell's palsy were identified with an overall incidence of 37.7/100 000 person-years. The 2004 Cochrane reviews were associated with immediate falls in prednisolone therapy (−6.3% (−11.0 to −1.6)), rising trends in combination therapy (1.1% per quarter (0.5 to 1.7)) and falling trends for untreated cases (−0.8% per quarter (−1.4 to −0.3)). SBPS was associated with immediate increases in prednisolone therapy (5.1% (0.9 to 9.3)) and rising trends in prednisolone therapy (0.7% per quarter (0.4 to 1.2)); falling trends in combination therapy (−1.7% per quarter (−2.2 to −1.3)); and rising trends for untreated cases (1.2% per quarter (0.8 to 1.6)). Despite improvements, 44% still remain untreated. Conclusions SBPS was clearly associated with change in management, but a significant proportion of patients failed to receive effective treatment, which cannot be fully explained. Clarity and uncertainty in

Findings of an international study on burnup credit

International Nuclear Information System (INIS)

Brady, M.C.; Takano, M.; Okuno, H.; DeHart, M.D.; Nouri, A.

1996-01-01

Findings from a four year study by an international benchmarking group in the comparison of computational methods for evaluating burnup credit in criticality safety analyses are presented in this paper. Approximately 20 participants from 11 countries have provided results for most problems. Four detailed benchmark problems for Pressurized Water Reactor (PWR) fuel have been completed and are summarized in this paper. Preliminary results from current work addressing burnup credit for Boiling Water Reactor (BWR) fuel will also be discussed as well as planned activities for additional benchmarks including Mixed-Oxide (MOX) fuels, subcritical benchmarks, international databases, and other activities

Almagest, a new trackless ring finding algorithm

Energy Technology Data Exchange (ETDEWEB)

Lamanna, G., E-mail: gianluca.lamanna@cern.ch

2014-12-01

A fast ring finding algorithm is a crucial point to allow the use of RICH in on-line trigger selection. The present algorithms are either too slow (with respect to the incoming data rate) or need the information coming from a tracking system. Digital image techniques, assuming limited computing power (as for example Hough transform), are not perfectly robust for what concerns the noise immunity. We present a novel technique based on Ptolemy's theorem for multi-ring pattern recognition. Starting from purely geometrical considerations, this algorithm (also known as “Almagest”) allows fast and trackless rings reconstruction, with spatial resolution comparable with other offline techniques. Almagest is particularly suitable for parallel implementation on multi-cores machines. Preliminary tests on GPUs (multi-cores video card processors) show that, thanks to an execution time smaller than 10 μs per event, this algorithm could be employed for on-line selection in trigger systems. The user case of the NA62 RICH trigger, based on GPU, will be discussed. - Highlights: • A new algorithm for fast multiple ring searching in RICH detectors is presented. • The Almagest algorithm exploits the computing power of Graphics processers (GPUs). • A preliminary implementation for on-line triggering in the NA62 experiment shows encouraging results.

Prison Therapeutic Community Treatment for Female Offenders: Profiles and Preliminary Findings for Mental Health and Other Variables (Crime, Substance Use and HIV Risk)

Science.gov (United States)

Sacks, Joann Y.; Sacks, Stanley; Mckendrick, Karen; Banks, Steven; Schoeneberger, Marlies; Hamilton, Zachary; Stommel, Joseph; Shoemaker, Joanie

2008-01-01

This random assignment study compared women in a prison Therapeutic Community (TC) program with those in a cognitive-behavioral intervention. Over two thirds of study subjects received a lifetime diagnosis of severe mental disorder, nearly one-half received a diagnosis of PTSD, and virtually all reported exposure to trauma. Preliminary analysis (n…

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia

2018-04-10

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.

78 FR 20615 - Drill Pipe From the People's Republic of China: Preliminary Results of Countervailing Duty...

Science.gov (United States)

2013-04-05

... regarding benefit; and section 771(5A) of the Act regarding specificity. In making these findings, we have... Operations to Paul Piquado, Assistant Secretary for Import Administration, ``Decision Memorandum for... China'' (Preliminary Decision Memorandum), dated currently with this notice, and hereby adopted by this...

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......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...

Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

Science.gov (United States)

Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

Real estate value prediction using multivariate regression models

Science.gov (United States)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Preliminary report on the Northern California Power Agency's Notice of Intention to seek certification for NCPA Geothermal Project No. 2

Energy Technology Data Exchange (ETDEWEB)

1978-01-01

This preliminary report on the Northern California Power Agency (NCPA) geothermal power plant proposal has been prepared pursuant to California Public Resources Code Sections 25510, 25512, and 25540. It presents the preliminary Findings of fact and Conclusions adopted by the Commission Committee assigned to conduct proceedings on the Notice. In addition, the report contains a description of the proposed project, a summary of the proceedings to date, and local, state, and Federal government agency comments on the proposal. Finally, the report presents the Committee's view of those issues that require further consideration in future proceedings on the Notice. Pursuant to Public Resources Code Sections 25512 and 25540, the report presents preliminary Findings and Conclusions on: (1) conformity to the forecast of statewide and service area electric power demands; (2) the degree to which the proposed site and facility conform with applicable local, regional, state and Federal standards, ordinances, and laws; and (3) the safety and reliability of the facility.

Computing multiple-output regression quantile regions

Czech Academy of Sciences Publication Activity Database

Paindaveine, D.; Šiman, Miroslav

2012-01-01

Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf

Preface to Berk's "Regression Analysis: A Constructive Critique"

OpenAIRE

de Leeuw, Jan

2003-01-01

It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...

Witness for Wellness: preliminary findings from a community-academic participatory research mental health initiative.

Science.gov (United States)

Bluthenthal, Ricky N; Jones, Loretta; Fackler-Lowrie, Nicole; Ellison, Marcia; Booker, Theodore; Jones, Felica; McDaniel, Sharon; Moini, Moraya; Williams, Kamau R; Klap, Ruth; Koegel, Paul; Wells, Kenneth B

2006-01-01

Quality improvement programs promoting depression screening and appropriate treatment can significantly reduce racial and ethnic disparities in mental-health care and outcomes. However, promoting the adoption of quality-improvement strategies requires more than the simple knowledge of their potential benefits. To better understand depression issues in racial and ethnic minority communities and to discover, refine, and promote the adoption of evidence-based interventions in these communities, a collaborative academic-community participatory partnership was developed and introduced through a community-based depression conference. This partnership was based on the community-influenced model used by Healthy African-American Families, a community-based agency in south Los Angeles, and the Partners in Care model developed at the UCLA/RAND NIMH Health Services Research Center. The integrated model is described in this paper as well as the activities and preliminary results based on multimethod program evaluation techniques. We found that combining the two models was feasible. Significant improvements in depression identification, knowledge about treatment options, and availability of treatment providers were observed among conference participants. In addition, the conference reinforced in the participants the importance of community mobilization for addressing depression and mental health issues in the community. Although the project is relatively new and ongoing, already substantial gains in community activities in the area of depression have been observed. In addition, new applications of this integrated model are underway in the areas of diabetes and substance abuse. Continued monitoring of this project should help refine the model as well as assist in the identification of process and outcome measures for such efforts.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia; Rue, Haavard

2018-01-01

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite

A preliminary study of mercury exposure and blood pressure in the Brazilian Amazon

Directory of Open Access Journals (Sweden)

Guimarães Jean

2006-10-01

Full Text Available Abstract Background Fish is considered protective for coronary heart disease (CHD, but mercury (Hg intake from fish may counterbalance beneficial effects. Although neurotoxic effects of methylmercury (MeHg are well established, cardiovascular effects are still debated. The objective of the present study was to evaluate blood pressure in relation to Hg exposure and fish consumption among a non-indigenous fish-eating population in the Brazilian Amazon. Methods The study was conducted among 251 persons from six communities along the Tapajós River, a major tributary of the Amazon. Data was obtained for socio-demographic information, fish consumption, height and weight to determine body mass index (BMI, systolic and diastolic blood pressure, and Hg concentration in hair samples. Results Results showed that overall, systolic and diastolic blood pressure, were relatively low (mean: 113.9 mmHg ± 14.6 and 73.7 mmHg ± 11.0. Blood pressure was significantly associated with hair total Hg (H-Hg, age, BMI and gender. No association was observed between fish consumption and blood pressure, although there were significant inter-community differences. Logistic regression analyses showed that the Odds Ratio (OR for elevated systolic blood pressure (≥ 130 mmHg with H-Hg ≥ 10 μg/g was 2.91 [1.26–7.28], taking into account age, BMI, smoking, gender and community. Conclusion The findings of this preliminary study add further support for Hg cardiovascular toxicity.

Linear Regression Analysis

CERN Document Server

Seber, George A F

2012-01-01

Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.

Moderation analysis using a two-level regression model.

Science.gov (United States)

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Renfu Jia

2016-01-01

Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

A preliminary investigation of the relationship between dispositional mindfulness and eating disorder symptoms among men in residential substance use treatment.

Science.gov (United States)

Elmquist, JoAnna; Shorey, Ryan C; Anderson, Scott E; Stuart, Gregory L

2017-01-01

The comorbidity between eating disorders (EDs) and substance use disorders (SUDs) is of particular concern given the high rates of mortality, relapse and poor treatment outcomes associated with both disorders. As a result, there has been a growing impetus within the field to elucidate factors that might influence and aid treatment for this comorbidity. One such factor is dispositional mindfulness, as past literature has demonstrated a significant relationship between mindfulness and both EDs and SUDs. However, we are unaware of any research that has examined the relationship between dispositional mindfulness and ED symptoms in a sample of men in residential treatment for SUDs. Medical records from 152 men were included in the current study. Alcohol and drug use and problems, ED symptoms, and dispositional mindfulness were assessed with self-report measures. Hierarchical regression analysis indicated that dispositional mindfulness was inversely related to ED symptoms after controlling for alcohol use, drug use, and age. Although results are preliminary and continued research in this area is needed, our findings suggest that there may be potential usefulness in targeting and enhancing mindfulness among patients in residential treatment for SUDs with co-occurring psychiatric symptoms (e.g., EDs).

23 CFR 645.109 - Preliminary engineering.

Science.gov (United States)

2010-04-01

... 23 Highways 1 2010-04-01 2010-04-01 false Preliminary engineering. 645.109 Section 645.109 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION ENGINEERING AND TRAFFIC OPERATIONS UTILITIES Utility Relocations, Adjustments, and Reimbursement § 645.109 Preliminary engineering. (a) As...

Preliminary report on the Pacific Gas and Electric Company's notice of intention to seek certification for Geysers Unit 16 (78-NOI-6)

Energy Technology Data Exchange (ETDEWEB)

1979-01-01

The preliminary findings of fact and conclusion adopted by the Commission Committee are presented. Also, a description of the proposed project, a summary of the proceedings to date, and local, state, and federal government agency comments on the proposal are included. Preliminary findings and conclusions are presented on: (a) conformity to the forecast of statewide and service area electric power demands; (b) the degree to which the proposed site and facility conform with applicable local, regional, state, and federal standards, ordinances and laws; (c) the safety and reliability of the facility; and (d) the relative merit of the proposed transmission line corridors. (MHR)

Preliminary report on the Department of Water Resources Notice of Intention to file an application for certification of DWR Bottle Rock, 78-NOI-7

Energy Technology Data Exchange (ETDEWEB)

1979-01-01

The preliminary findings of fact and conclusion adopted by the Commission Committee are presented. Also, a description of the proposed project, a summary of the proceedings to date, and local, state, and federal government agency comments on the proposal are included. Preliminary findings and conclusions are presented on: (a) conformity to the forecast of statewide and service area electric power demands; (b) the degree to which the proposed site and facility conform with applicable local, regional, state, and federal standards, ordinances and laws; (c) the safety and reliability of the facility; and (d) the relative merit of the proposed transmission line corridors. (MHR)

Offending by people with intellectual disabilities in community settings: a preliminary examination of contextual factors.

Science.gov (United States)

Wheeler, Jessica R; Clare, Isabel C H; Holland, Anthony J

2013-09-01

While several validated measures of the life circumstances of people with intellectual disabilities (ID) have been developed, this stream of research has not yet been well integrated with environmentally oriented criminological theory to explain offending among people with ID. In this study, we attempt to provide a preliminary integration through an investigation of the relationship between contemporary life experiences, well-being, choice and offending among people with ID, exploring the relevance of two classic criminological theories (theories of strain and social control). Questionnaire measures were used to compare a range of 'ordinary' life experiences [the 'Life Experiences Checklist' (LEC)], subjective well-being (the 'Personal Well-being Index - ID') and the extent of choice (the 'Choice Questionnaire'), between offenders (N = 27) and non-offenders (N = 19) with ID recruited through integrated (NHS and Local Authority) multi-disciplinary teams (community teams for adults with learning disabilities). Using regression analyses to explore the strength of associations with offending, it was found that an indicator of impoverished personal relationships, from the LEC provided the best predictor of offending. This finding appears to favour criminological explanations based on social control. Existing measures of life circumstances can be used to explore environmentally oriented criminological theories, bringing benefits to our understanding and treatment of offenders with ID living in community settings. © 2013 John Wiley & Sons Ltd.

Kepler AutoRegressive Planet Search

Science.gov (United States)

Caceres, Gabriel Antonio; Feigelson, Eric

2016-01-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real

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.

Preliminary design county plan Zeeland

International Nuclear Information System (INIS)

1987-01-01

The preliminary design 'Streekplan Zeeland' (Country plan Zeeland, with regard to the location of additional nuclear power plants in Zeeland, the Netherlands) has passed through a consultation and participation round. Thereupon 132 reactions have been received. These have been incorporated and answered in two notes. This proposal deals with the principal points of the preliminary design and treats also the remarks of the committees Environmental (town and country) Planning (RO), Provincial (town and country) Planning Committee (PPC) and Association of Communities of Zeeland (VZG), on the reply notes. The preliminary design with the modifications, collected in appendix 3, is proposed to be the starting point in the drawing-up of the design-country-plan. This design subsequently will pass the formal country-plan procedure. (author). 1 fig

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Science.gov (United States)

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Demonstration of a Fiber Optic Regression Probe

Science.gov (United States)

Korman, Valentin; Polzin, Kurt A.

2010-01-01

The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for

Caudal regression syndrome : a case report

International Nuclear Information System (INIS)

Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun

1998-01-01

Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging

Caudal regression syndrome : a case report

Energy Technology Data Exchange (ETDEWEB)

Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun [Chungang Gil Hospital, Incheon (Korea, Republic of)

1998-07-01

Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging.

Preliminary evaluation of data mining on non-masslike enhancement of breast lesions on MRI

International Nuclear Information System (INIS)

Tan Hongna; Li Ruimin; Wang Peihua; Tang Feng; Mao Jian; Shen Xigang; Qian Min; Gu Yajia; Su Yi; Chen Ying

2009-01-01

Objective: To evaluate the diagnostic values of the breast imaging reporting and data system-MRI (BI-RADS-MRI)description about non-masslike enhancement by data mining. Methods: Fifty- five patients with non-masslike enhancement lesions showed on breast contrast-enhanced MRI were evaluated using two data mining algorithms (Logistic regression and decision tree) and 10-fold cross-validation methods. Results: There were 28 malignant and 27 benign lesions. The most frequent findings of the malignant lesions were clustered ring enhancement and clumped enhancement [12 and 4 lesions, respectively; 84.2% (16/19) in decision trees, partial regression coefficients in Logistic model were 2.128 and 1.723, respectively], whereas homogenous, stippled, reticular internal and linear ductal enhancement were the most frequent findings in benign lesions [4,9,1 and 7 lesions, respectively; 72.4% (21/29) in decision tree, partial regression coefficients in Logistic model were 0.357 (homogenous), 1.861 (stippled) and 18.870(reticular), respectively]. 10-fold cross-validation indicated that decision tree (C5.0) achieved an accuracy of 69.3% with a sensitivity of 66.7% and a specificity of 71.7% in comparison to the Logistic regression model with an accuracy of 57.0%, a sensitivity of 43.3% and a specificity of 71.7%. Conclusions: The diagnosis efficacy of non-masslike enhancement interpretation according to BI-RADS-MRI is not high. It is very important to find more potential features of non-masslike enhancement to improve the diagnosis accuracy. (authors)

Correlation and simple linear regression.

Science.gov (United States)

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Preliminary spatial analysis of combined BATSE/Ulysses gamma-ray burst locations

International Nuclear Information System (INIS)

Kippen, R. Marc; Hurley, Kevin; Pendleton, Geoffrey N.

1998-01-01

We present the preliminary spatial analysis of 278 bursts that have been localized by BATSE and the two-spacecraft Compton/Ulysses Interplanetary Network. The large number and superior accuracy of the combined BATSE/Ulysses locations provides improved sensitivity to small-angle source properties. We find that the locations are consistent with large- and small-scale isotropy, with no significant small-angle clustering. We constrain the fraction of sources in clusters and discuss the implications for burst repetition

Ambient Assisted Living and ageing: preliminary results of RITA project.

Science.gov (United States)

Aquilano, Michela; Cavallo, Filippo; Bonaccorsi, Manuele; Esposito, Raffaele; Rovini, Erika; Filippi, Massimo; Esposito, Dario; Dario, Paolo; Carrozza, Maria Chiara

2012-01-01

The ageing of population is a social phenomenon that most of worldwide countries are facing. They are, and will be even more in the future, indeed trying to find solutions for improving quality of life of their elderly citizens. The project RITA wants to demonstrate that an update of the current socio-medical services with an Ambient Assisted Living (AAL) approach could improve the service efficiency and the quality of life of both elderly and caregiver. This paper presents the preliminary results obtained in RITA.

bayesQR: A Bayesian Approach to Quantile Regression

Directory of Open Access Journals (Sweden)

Dries F. Benoit

2017-01-01

Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

Exploring factors associated with traumatic dental injuries in preschool children: a Poisson regression analysis.

Science.gov (United States)

Feldens, Carlos Alberto; Kramer, Paulo Floriani; Ferreira, Simone Helena; Spiguel, Mônica Hermann; Marquezan, Marcela

2010-04-01

This cross-sectional study aimed to investigate the factors associated with dental trauma in preschool children using Poisson regression analysis with robust variance. The study population comprised 888 children aged 3- to 5-year-old attending public nurseries in Canoas, southern Brazil. Questionnaires assessing information related to the independent variables (age, gender, race, mother's educational level and family income) were completed by the parents. Clinical examinations were carried out by five trained examiners in order to assess traumatic dental injuries (TDI) according to Andreasen's classification. One of the five examiners was calibrated to assess orthodontic characteristics (open bite and overjet). Multivariable Poisson regression analysis with robust variance was used to determine the factors associated with dental trauma as well as the strengths of association. Traditional logistic regression was also performed in order to compare the estimates obtained by both methods of statistical analysis. 36.4% (323/888) of the children suffered dental trauma and there was no difference in prevalence rates from 3 to 5 years of age. Poisson regression analysis showed that the probability of the outcome was almost 30% higher for children whose mothers had more than 8 years of education (Prevalence Ratio = 1.28; 95% CI = 1.03-1.60) and 63% higher for children with an overjet greater than 2 mm (Prevalence Ratio = 1.63; 95% CI = 1.31-2.03). Odds ratios clearly overestimated the size of the effect when compared with prevalence ratios. These findings indicate the need for preventive orientation regarding TDI, in order to educate parents and caregivers about supervising infants, particularly those with increased overjet and whose mothers have a higher level of education. Poisson regression with robust variance represents a better alternative than logistic regression to estimate the risk of dental trauma in preschool children.

Utility of Social Modeling for Proliferation Assessment - Preliminary Assessment

Energy Technology Data Exchange (ETDEWEB)

Coles, Garill A.; Gastelum, Zoe N.; Brothers, Alan J.; Thompson, Sandra E.

2009-06-01

This Preliminary Assessment draft report will present the results of a literature search and preliminary assessment of the body of research, analysis methods, models and data deemed to be relevant to the Utility of Social Modeling for Proliferation Assessment research. This report will provide: 1) a description of the problem space and the kinds of information pertinent to the problem space, 2) a discussion of key relevant or representative literature, 3) a discussion of models and modeling approaches judged to be potentially useful to the research, and 4) the next steps of this research that will be pursued based on this preliminary assessment. This draft report represents a technical deliverable for the NA-22 Simulations, Algorithms, and Modeling (SAM) program. Specifically this draft report is the Task 1 deliverable for project PL09-UtilSocial-PD06, Utility of Social Modeling for Proliferation Assessment. This project investigates non-traditional use of social and cultural information to improve nuclear proliferation assessment, including nonproliferation assessment, proliferation resistance assessments, safeguards assessments and other related studies. These assessments often use and create technical information about the State’s posture towards proliferation, the vulnerability of a nuclear energy system to an undesired event, and the effectiveness of safeguards. This project will find and fuse social and technical information by explicitly considering the role of cultural, social and behavioral factors relevant to proliferation. The aim of this research is to describe and demonstrate if and how social science modeling has utility in proliferation assessment.

Flexible link functions in nonparametric binary regression with Gaussian process priors.

Science.gov (United States)

Li, Dan; Wang, Xia; Lin, Lizhen; Dey, Dipak K

2016-09-01

In many scientific fields, it is a common practice to collect a sequence of 0-1 binary responses from a subject across time, space, or a collection of covariates. Researchers are interested in finding out how the expected binary outcome is related to covariates, and aim at better prediction in the future 0-1 outcomes. Gaussian processes have been widely used to model nonlinear systems; in particular to model the latent structure in a binary regression model allowing nonlinear functional relationship between covariates and the expectation of binary outcomes. A critical issue in modeling binary response data is the appropriate choice of link functions. Commonly adopted link functions such as probit or logit links have fixed skewness and lack the flexibility to allow the data to determine the degree of the skewness. To address this limitation, we propose a flexible binary regression model which combines a generalized extreme value link function with a Gaussian process prior on the latent structure. Bayesian computation is employed in model estimation. Posterior consistency of the resulting posterior distribution is demonstrated. The flexibility and gains of the proposed model are illustrated through detailed simulation studies and two real data examples. Empirical results show that the proposed model outperforms a set of alternative models, which only have either a Gaussian process prior on the latent regression function or a Dirichlet prior on the link function. © 2015, The International Biometric Society.

Analyses of Developmental Rate Isomorphy in Ectotherms: Introducing the Dirichlet Regression.

Directory of Open Access Journals (Sweden)

David S Boukal

Full Text Available Temperature drives development in insects and other ectotherms because their metabolic rate and growth depends directly on thermal conditions. However, relative durations of successive ontogenetic stages often remain nearly constant across a substantial range of temperatures. This pattern, termed 'developmental rate isomorphy' (DRI in insects, appears to be widespread and reported departures from DRI are generally very small. We show that these conclusions may be due to the caveats hidden in the statistical methods currently used to study DRI. Because the DRI concept is inherently based on proportional data, we propose that Dirichlet regression applied to individual-level data is an appropriate statistical method to critically assess DRI. As a case study we analyze data on five aquatic and four terrestrial insect species. We find that results obtained by Dirichlet regression are consistent with DRI violation in at least eight of the studied species, although standard analysis detects significant departure from DRI in only four of them. Moreover, the departures from DRI detected by Dirichlet regression are consistently much larger than previously reported. The proposed framework can also be used to infer whether observed departures from DRI reflect life history adaptations to size- or stage-dependent effects of varying temperature. Our results indicate that the concept of DRI in insects and other ectotherms should be critically re-evaluated and put in a wider context, including the concept of 'equiproportional development' developed for copepods.

Discussion: some new findings from surface subsidence monitoring over longwall panels

International Nuclear Information System (INIS)

Luo, Y.; Peng, S.S.; Arioglu, E.

1992-01-01

The article consists of a discussion of the paper, 'some new findings from surface subsidence monitoring over longwall panels' and a reply by the paper's authors, Luo and Peng. The reviewer, Arioglu, regards the paper favourably but suggests that surface subsidence can be represented by an exponential expression, and that there is a regression equation linking possible subsidence, pillar loading and the height-to-width ratio of the pillars left. Luo and Peng reply with their reasons for preferring their original linear regression model to the non-linear models suggested by Arioglu. 4 figs

Exploratory shaft facility preliminary designs - Paradox Basin. Technical report

International Nuclear Information System (INIS)

1983-09-01

The purpose of the Preliminary Design Report, Paradox Basin, is to provide a description of the preliminary design for an Exploratory Shaft Facility in the Paradox Basin, Utah. This issue of the report describes the preliminary design for constructing the exploratory shaft using the Large Hole Drilling Method of construction and outlines the preliminary design and estimates of probable construction cost. The Preliminary Design Report is prepared to complement and summarize other documents that comprise the design at the preliminary stage of completion, December 1982. Other design documents include drawings, cost estimates and schedules. The preliminary design drawing package, which includes the construction schedule drawing, depicts the descriptions in this report. For reference, a list of the drawing titles and corresponding numbers is included in the Appendix. The report is divided into three principal sections: Design Basis, Facility Description, and Construction Cost Estimate. 30 references

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

Science.gov (United States)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Preliminary Study of 20 MWth Experiment Power Reactor based on Pebble Bed Reactor

Science.gov (United States)

Irwanto, Dwi; Permana, Sidik; Pramuditya, Syeilendra

2017-07-01

In this study, preliminary design calculations for experimental small power reactor (20 MWt) based on Pebble Bed Reactor (PBR) are performed. PBR technology chosen due to its advantages in neutronic and safety aspects. Several important parameters, such as fissile enrichment, number of fuel passes, burnup and effective multiplication factor are taken into account in the calculation to find neutronic characteristics of the present reactor design.

Preliminary findings on the association between attachment patterns and levels of growth hormone in a sample of children with non-organic failure to thrive

Science.gov (United States)

Fojanesi, Marta; Gallo, Mariana; Spaziani, Matteo; Russo, Federica; Valentini, Martina; Bersani, Francesco Saverio; Biondi, Massimo; Radicioni, Antonio

2018-01-01

Deficiency of growth hormone (GH) in absence of pituitary injuries is one of the causes of short stature and of the non organic failure to thrive (NOFTT) condition. Advances in developmental psychology have highlighted the role of emotions and caregiving behaviors in the organization of child’s personality and psychobiology, with the mother-son attachment bond being considered a fundamental developmental experience. The objective of the present preliminary study was to assess whether there are significant correlations between attachment patterns and GH levels in a sample of subjects with NOFTT. Overall, 27 children (mean age 9.49±2.63 years) with NOFTT were enrolled. Perceived attachment security was assessed through the Security Scale (SS) and its subscales focused on maternal and paternal security. Pearson partial correlation was used to test associations between GH levels and SS measures adjusting for confounding factors (i.e. age, gender and body mass index). Across all subjects, GH was significantly positively correlated with general security (r=0.425; p=0.038) and maternal security (r=0.451; p=0.027) and not significantly correlated with paternal security (r=0.237; p=0.264). These findings preliminarily suggest that the association between GH levels and perceived attachment security may play a role in the pathophysiology of NOFTT and add to the accumulating evidence that attachment patterns may be related with specific psychoendocrine underpinnings.

Background stratified Poisson regression analysis of cohort data.

Science.gov (United States)

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Variable importance in latent variable regression models

NARCIS (Netherlands)

Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

2014-01-01

The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

Regression: The Apple Does Not Fall Far From the Tree.

Science.gov (United States)

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Multiple regression and beyond an introduction to multiple regression and structural equation modeling

CERN Document Server

Keith, Timothy Z

2014-01-01

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI.

Science.gov (United States)

Wong, Chi Wah; Olafsson, Valur; Tal, Omer; Liu, Thomas T

2012-10-15

Resting-state functional connectivity magnetic resonance imaging is proving to be an essential tool for the characterization of functional networks in the brain. Two of the major networks that have been identified are the default mode network (DMN) and the task positive network (TPN). Although prior work indicates that these two networks are anti-correlated, the findings are controversial because the anti-correlations are often found only after the application of a pre-processing step, known as global signal regression, that can produce artifactual anti-correlations. In this paper, we show that, for subjects studied in an eyes-closed rest state, caffeine can significantly enhance the detection of anti-correlations between the DMN and TPN without the need for global signal regression. In line with these findings, we find that caffeine also leads to widespread decreases in connectivity and global signal amplitude. Using a recently introduced geometric model of global signal effects, we demonstrate that these decreases are consistent with the removal of an additive global signal confound. In contrast to the effects observed in the eyes-closed rest state, caffeine did not lead to significant changes in global functional connectivity in the eyes-open rest state. Copyright © 2012 Elsevier Inc. All rights reserved.

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

Identification of Sexually Abused Female Adolescents at Risk for Suicidal Ideations: A Classification and Regression Tree Analysis

Science.gov (United States)

Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois

2013-01-01

This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…

Influence diagnostics in meta-regression model.

Science.gov (United States)

Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

2017-09-01

This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

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.

Regression filter for signal resolution

International Nuclear Information System (INIS)

Matthes, W.

1975-01-01

The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)

32 CFR 644.30 - Preliminary real estate work.

Science.gov (United States)

2010-07-01

... 32 National Defense 4 2010-07-01 2010-07-01 true Preliminary real estate work. 644.30 Section 644... PROPERTY REAL ESTATE HANDBOOK Project Planning Military (army and Air Force) and Other Federal Agencies § 644.30 Preliminary real estate work. (a) Preliminary real estate work is defined as that action taken...

Direction of Effects in Multiple Linear Regression Models.

Science.gov (United States)

Wiedermann, Wolfgang; von Eye, Alexander

2015-01-01

Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

Robust mislabel logistic regression without modeling mislabel probabilities.

Science.gov (United States)

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Preliminary Estimation of Kappa Parameter in Croatia

Science.gov (United States)

Stanko, Davor; Markušić, Snježana; Ivančić, Ines; Mario, Gazdek; Gülerce, Zeynep

2017-12-01

Spectral parameter kappa κ is used to describe spectral amplitude decay “crash syndrome” at high frequencies. The purpose of this research is to estimate spectral parameter kappa for the first time in Croatia based on small and moderate earthquakes. Recordings of local earthquakes with magnitudes higher than 3, epicentre distances less than 150 km, and focal depths less than 30 km from seismological stations in Croatia are used. The value of kappa was estimated from the acceleration amplitude spectrum of shear waves from the slope of the high-frequency part where the spectrum starts to decay rapidly to a noise floor. Kappa models as a function of a site and distance were derived from a standard linear regression of kappa-distance dependence. Site kappa was determined from the extrapolation of the regression line to a zero distance. The preliminary results of site kappa across Croatia are promising. In this research, these results are compared with local site condition parameters for each station, e.g. shear wave velocity in the upper 30 m from geophysical measurements and with existing global shear wave velocity - site kappa values. Spatial distribution of individual kappa’s is compared with the azimuthal distribution of earthquake epicentres. These results are significant for a couple of reasons: to extend the knowledge of the attenuation of near-surface crust layers of the Dinarides and to provide additional information on the local earthquake parameters for updating seismic hazard maps of studied area. Site kappa can be used in the re-creation, and re-calibration of attenuation of peak horizontal and/or vertical acceleration in the Dinarides area since information on the local site conditions were not included in the previous studies.

Stuttering on function words in bilingual children who stutter: A preliminary study.

Science.gov (United States)

Gkalitsiou, Zoi; Byrd, Courtney T; Bedore, Lisa M; Taliancich-Klinger, Casey L

2017-01-01

Evidence suggests young monolingual children who stutter (CWS) are more disfluent on function than content words, particularly when produced in the initial utterance position. The purpose of the present preliminary study was to investigate whether young bilingual CWS present with this same pattern. The narrative and conversational samples of four bilingual Spanish- and English-speaking CWS were analysed. All four bilingual participants produced significantly more stuttering on function words compared to content words, irrespective of their position in the utterance, in their Spanish narrative and conversational speech samples. Three of the four participants also demonstrated more stuttering on function compared to content words in their narrative speech samples in English, but only one participant produced more stuttering on function than content words in her English conversational sample. These preliminary findings are discussed relative to linguistic planning and language proficiency and their potential contribution to stuttered speech.

Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components

OpenAIRE

Geroukis, Asterios; Brorson, Erik

2014-01-01

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...

Quality Cost in the Construction Industry ' Preliminary Findings in Malaysia

Directory of Open Access Journals (Sweden)

Mukhtar Che Ali

2010-06-01

Full Text Available One of the key areas being emphasis in ISO 9001 Quality Management System (QMS is performance measurement towards continual improvement. Among the primary measuring tools is quality cost approach. Quality cost has been well practice in manufacturing sector but slowly gain its importance in construction industry. In fact Project Management Body of Knowledge (PMBOK has reckoned quality cost as one of the tool and technique in few of its management processes. In view of such circumstances that has prompted an effort to undertake a study to ascertain the level of knowledge and practice on quality cost in Malaysian construction landscape. The targeted group of respondents was the personnel in the project management team. Capitalizing Construction Industry Development Board (CIDB National Electronic Tendering Initiatives (NETI road shows which were held in year 2007 throughout the country, the author was able to garner 263 respondents representing the project management team. Subsequently the data gathered from the completed forms were analyzed using Statistical Package for Social Science (SPSS software. General findings indicated that the level of knowledge and practice on quality cost among the project management team were relatively low. One of the main contributing factors was poor knowledge in the area related to quality cost. Despite of such scenario most of the respondents showed their interest in acquiring knowledge in the field of quality cost. Hence quality cost approach is at the infancy stage in Malaysian construction industry.

Spontaneous regression of epithelial downgrowth from clear corneal phacoemulsification wound

Directory of Open Access Journals (Sweden)

Ryan M. Jaber

2018-06-01

Full Text Available Purpose: To report a case of spontaneous regression of optical coherence tomography (OCT and confocal microscopy-supported epithelial downgrowth associated with clear corneal phacoemulsification wound. Observations: A 66-year-old Caucasian male presented two years after phacoemulsification in the left eye with an enlarging cornea endothelial lesion in that eye. His early post-operative course had been complicated by corneal edema and iris transillumination defects. The patient presented to our clinic with a large geographic sheet of epithelial downgrowth and iris synechiae to the temporal clear corneal wound. His vision was correctable to 20/25 in his left eye. Anterior segment OCT showed a hyperreflective layer on the posterior cornea with an abrupt transition that corresponded to the clinical transition zone of the epithelial downgrowth. Confocal microscopy showed polygonal cells with hyperreflective nuclei suggestive of epithelial cells in the area of the lesion with a transition to a normal endothelial cell mosaic. Given the lack of glaucoma or inflammation and the relatively good vision, the plan was made to closely monitor for progression with the anticipation that he may require aggressive surgery. Over course of subsequent follow-up visits at three, seven and ten months; the endothelial lesion receded significantly. Confocal imaging in the area of the previously affected cornea showed essentially normal morphology with anan endothelial cell count of 1664 cells/mm2. Conclusions and importance: Epithelial downgrowth may spontaneously regress. Though the mechanism is yet understood, contact inhibition of movement may play a role. Despite this finding, epithelial downgrowth is typically a devastating process requiring aggressive treatment. Keywords: Epithelial downgrowth, Spontaneous regression, Confocal microscopy, Contact inhibition of movement

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,…

Hierarchical regression analysis in structural Equation Modeling

NARCIS (Netherlands)

de Jong, P.F.

1999-01-01

In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main

Repeated Results Analysis for Middleware Regression Benchmarking

Czech Academy of Sciences Publication Activity Database

Bulej, Lubomír; Kalibera, T.; Tůma, P.

2005-01-01

Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005

WISE/NEOWISE OBSERVATIONS OF THE JOVIAN TROJANS: PRELIMINARY RESULTS

International Nuclear Information System (INIS)

Grav, T.; Mainzer, A. K.; Bauer, J.; Masiero, J.; Eisenhardt, P. R. M.; Blauvelt, E.; DeBaun, E.; Elsbury, D.; Gautier, T. IV; Gomillion, S.; Hand, E.; Wilkins, A.; Spahr, T.; McMillan, R. S.; Walker, R.; Cutri, R.; Wright, E.

2011-01-01

We present the preliminary analysis of over 1739 known and 349 candidate Jovian Trojans observed by the NEOWISE component of the Wide-field Infrared Survey Explorer (WISE). With this survey the available diameters, albedos, and beaming parameters for the Jovian Trojans have been increased by more than an order of magnitude compared to previous surveys. We find that the Jovian Trojan population is very homogenous for sizes larger than ∼10 km (close to the detection limit of WISE for these objects). The observed sample consists almost exclusively of low albedo objects, having a mean albedo value of 0.07 ± 0.03. The beaming parameter was also derived for a large fraction of the observed sample, and it is also very homogenous with an observed mean value of 0.88 ± 0.13. Preliminary debiasing of the survey shows that our observed sample is consistent with the leading cloud containing more objects than the trailing cloud. We estimate the fraction to be N(leading)/N(trailing) ∼ 1.4 ± 0.2, lower than the 1.6 ± 0.1 value derived by Szabó et al.

Environmental Survey preliminary report, Savannah River Plant, Aiken, South Carolina

Energy Technology Data Exchange (ETDEWEB)

1987-08-01

This report contains the preliminary findings based on the first phase of an Environmental Survey at the Department of Energy (DOE) Savannah River Plant (SRP), located at Aiken, South Carolina. The Survey is being conducted by DOE's Office of Environment, Safety and Health. The following topics are discussed: general site information; air, soil, surface water and ground water; hydrogeology; waste management; toxic and chemical materials; release of tritium oxides; radioactivity in milk; contamination of ground water and wildlife; pesticide use; and release of radionuclides into seepage basins. 149 refs., 44 figs., 53 tabs.

Foregrounds in the BOOMERANG-LDB data: a preliminary rms analysis

OpenAIRE

Masi, S.; Ade, P. A. R.; Bock, J.; Boscaleri, A.; Crill, B. P.; de Bernardis, P.; Ganga, K.; Giacometti, M.; Hivon, E.; Hristov, V. V.; Lange, A. E.; Martinis, L.; Mauskopf, P. D.; Montroy, T.; Netterfield, C. B.

2000-01-01

We present a preliminary analysis of the BOOMERanG LDB maps, focused on foregrounds. BOOMERanG detects dust emission at moderately low galactic latitudes ($b > -20^o$) in bands centered at 90, 150, 240, 410 GHz. At higher Galactic latitudes, we use the BOOMERanG data to set conservative upper limits on the level of contamination at 90 and 150 GHz. We find that the mean square signal correlated with the IRAS/DIRBE dust template is less than 3% of the mean square signal due to CMB anisotropy.

and Multinomial Logistic Regression

African Journals Online (AJOL)

This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).

The iron swords from the necropolis of Son Pellisser; preliminary advance

Directory of Open Access Journals (Sweden)

Agustín Fernández Mártinez

2016-11-01

Full Text Available This paper submits the iron swords from the necropolis of Son Pellisser (Calvià-Majorca. Although the materials that we find are numerous and still under research offer a preliminary study about three imported swords, found in burial objects, also their possible origin and way of introduction on the island. These weapons could be the first archaeological evidence that the Balearic mercenaries participated in the Wars of Sicily, between Carthaginians and Greeks, from the fifth century BC.

MRI findings in Tolosa-Hunt syndrome before and after systemic corticosteroid therapy

Energy Technology Data Exchange (ETDEWEB)

Cakirer, Sinan E-mail: scakirer@yahoo.com

2003-02-01

Tolosa-Hunt syndrome (THS) is characterized by painful ophthalmoplegia due to a granulomatous inflammation in the cavernous sinus. Corticosteroid therapy dramatically resolves both the clinical and radiological findings of THS. We present MRI findings of six patients with a clinical history of at least one episode of unilateral or bilateral orbital-periorbital pain, clinical findings of associated paresis of one or more of 3rd, 4th, 5th or 6th cranial nerves. All of the patients revealed an enlargement of the symptomatic cavernous sinus on magnetic resonance imaging (MRI) scans. Five patients revealed total resolution of the clinical findings within 1-8 weeks, following systemic corticosteroid treatment. One patient revealed only minor regression of clinical findings within 2 weeks after the initiation of the treatment, so the cavernous sinus lesion was reevaluated as meningioma on MRI, and the patient underwent surgical resection of the mass with resultant histopathological finding of cavernous sinus meningioma. A follow-up MRI scan was performed for five patients at the end of 8-weeks of steroid therapy. Three of these five patients showed total resolution of the cavernous sinus lesions whereas two of them revealed a partial regression of the cavernous sinus lesions. MRI findings before and after systemic corticosteroid therapy are important diagnostic criteria to put the definitive diagnosis of THS and to differentiate it from other cavernous sinus lesions that simulate THS both clinically and radiologically.

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.

Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression.

Science.gov (United States)

Song, Chao; Kwan, Mei-Po; Zhu, Jiping

2017-04-08

An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.

Background stratified Poisson regression analysis of cohort data

International Nuclear Information System (INIS)

Richardson, David B.; Langholz, Bryan

2012-01-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)

Regression of uveal malignant melanomas following cobalt-60 plaque. Correlates between acoustic spectrum analysis and tumor regression

International Nuclear Information System (INIS)

Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.

1985-01-01

Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque

Preliminary Report Regarding State Allocation Board Funding of the Los Angeles Unified School District's Belmont Learning Complex.

Science.gov (United States)

Armoudian, Maria; Carman, Georgann; Havan, Artineh; Heron, Frank

A preliminary report of the California Legislature's Joint Legislative Audit Committee presents findings on the construction team selection process for the Los Angeles Unified School District's (LAUSD's) Belmont Learning Complex. Evidence reveals a seriously flawed process that directly conflicted with existing law and practice. The report…

Convert a low-cost sensor to a colorimeter using an improved regression method

Science.gov (United States)

Wu, Yifeng

2008-01-01

Closed loop color calibration is a process to maintain consistent color reproduction for color printers. To perform closed loop color calibration, a pre-designed color target should be printed, and automatically measured by a color measuring instrument. A low cost sensor has been embedded to the printer to perform the color measurement. A series of sensor calibration and color conversion methods have been developed. The purpose is to get accurate colorimetric measurement from the data measured by the low cost sensor. In order to get high accuracy colorimetric measurement, we need carefully calibrate the sensor, and minimize all possible errors during the color conversion. After comparing several classical color conversion methods, a regression based color conversion method has been selected. The regression is a powerful method to estimate the color conversion functions. But the main difficulty to use this method is to find an appropriate function to describe the relationship between the input and the output data. In this paper, we propose to use 1D pre-linearization tables to improve the linearity between the input sensor measuring data and the output colorimetric data. Using this method, we can increase the accuracy of the regression method, so as to improve the accuracy of the color conversion.

Regression away from the mean: Theory and examples.

Science.gov (United States)

Schwarz, Wolf; Reike, Dennis

2018-02-01

Using a standard repeated measures model with arbitrary true score distribution and normal error variables, we present some fundamental closed-form results which explicitly indicate the conditions under which regression effects towards (RTM) and away from the mean are expected. Specifically, we show that for skewed and bimodal distributions many or even most cases will show a regression effect that is in expectation away from the mean, or that is not just towards but actually beyond the mean. We illustrate our results in quantitative detail with typical examples from experimental and biometric applications, which exhibit a clear regression away from the mean ('egression from the mean') signature. We aim not to repeal cautionary advice against potential RTM effects, but to present a balanced view of regression effects, based on a clear identification of the conditions governing the form that regression effects take in repeated measures designs. © 2017 The British Psychological Society.

On directional multiple-output quantile regression

Czech Academy of Sciences Publication Activity Database

Paindaveine, D.; Šiman, Miroslav

2011-01-01

Roč. 102, č. 2 (2011), s. 193-212 ISSN 0047-259X R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:Commision EC(BE) Fonds National de la Recherche Scientifique Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate quantile * quantile regression * multiple-output regression * halfspace depth * portfolio optimization * value-at risk Subject RIV: BA - General Mathematics Impact factor: 0.879, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/siman-0364128.pdf

Bayesian logistic regression analysis

NARCIS (Netherlands)

Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.

2012-01-01

In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

Science.gov (United States)

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Theory and Praxis in Community Based Language Development: preliminary findings from applications of the Guide for Planning the Future of Our Language

OpenAIRE

Eberhard David M.

2017-01-01

This study will provide a critique of preliminary results obtained from the application of the ‘Guide for Planning the Future of Our Language’ (Hanawalt, Varenkamp, Lahn, & Eberhard 2015) in minority speech communities. This recent methodological tool was developed to enable and empower minoritized language groups to do their own language planning and to control their own language development. The tool is based on a theoretical approach to community based language development known as the ‘Su...

Examination of influential observations in penalized spline regression

Science.gov (United States)

Türkan, Semra

2013-10-01

In parametric or nonparametric regression models, the results of regression analysis are affected by some anomalous observations in the data set. Thus, detection of these observations is one of the major steps in regression analysis. These observations are precisely detected by well-known influence measures. Pena's statistic is one of them. In this study, Pena's approach is formulated for penalized spline regression in terms of ordinary residuals and leverages. The real data and artificial data are used to see illustrate the effectiveness of Pena's statistic as to Cook's distance on detecting influential observations. The results of the study clearly reveal that the proposed measure is superior to Cook's Distance to detect these observations in large data set.

Correlation of parenting style and pediatric behavior guidance strategies in the dental setting: preliminary findings.

Science.gov (United States)

Aminabadi, Naser Asl; Farahani, Ramin Mostofi Zadeh

2008-04-01

The aim of this study was to investigate the influence of parenting style on the choice of proper behavior guidance strategies in pedodontics. Seventy-two children aged between 4 and 6 years (mean 5.12 years) with carious primary mandibular molars were selected. The Primary Caregivers' Practices Report (PCPR) was used to quantify authoritarian, permissive and authoritative aspects of the caregivers' parenting style. After inferior alveolar nerve block, carious lesions were removed and the teeth were restored using amalgam. The children's behavior during operation was assessed according to the sound, eye, and motor (SEM) scale. Communicative guidance, advance behavior guidance, parental separation, and deferred treatment were used for behavior management. The dominant authoritative score was observed in 50% of parents, permissive in 37.5%, and authoritarian in 12.5%. The mean SEM score in children belonging to authoritative parents was significantly lower than in children of permissive and of authoritarian parents (pparenting style. Advanced behavior guidance (protective stabilization) was applied in 16.7% of cases in the authoritative category and in 100% in the permissive and authoritarian categories. The use of restrictive devices (7.4%) and sedation (3.7%) was limited to the permissive category. Parental separation (40.7%) and deferred treatment (3.7%) were performed only in the permissive category. This study provides preliminary evidence that a child's reaction to restorative dental procedures is influenced by the nature of the caregiver's parenting style.

Farmers' Market Utilization among Supplemental Nutrition Assistance Program Recipients in New Orleans, Louisiana: Preliminary Findings.

Science.gov (United States)

Nuss, Henry; Skizim, Meg; Afaneh, Hasheemah; Miele, Lucio; Sothern, Melinda

2017-01-01

Farmers' markets are increasingly being promoted as a means to provide fresh produce to poor and underserved communities. However, farmers' market (FM) use remains low among low-income patrons. The purpose of our study was to examine FM awareness and use, grocery shopping behaviors, and internet use among Supplemental Nutrition Assistance Program (SNAP) recipients. A descriptive analysis of preliminary data was performed to evaluate quantitative baseline data among SNAP recipients between June and August 2016 in New Orleans, Louisiana (N=51). Data were collected via a 42-item online survey that included demographics, internet use, FM awareness and use, health information seeking behaviors and fruit and vegetable purchasing behaviors. Less than half of the survey respondents (n=24) had ever been to a FM. Local grocery stores and Wal-Mart were most used for purchasing fruits and vegetables (88% and 84%, respectively). The most common sources of healthy eating information were Women, Infants and Children (WIC) and the internet, frequently accessed via smartphones. More than 80% of participants were not aware that local FMs accepted electronic benefit transfer payments as a form of payment. These results support the incorporation of promotional methodology that combines internet-based mobile technology and existing services (eg, WIC) as a viable strategy to improve farmers' market use among low-income populations. As most participants were not aware that participating FMs accept electronic benefit transfer payments, this fact should be emphasized in promotional material.

Bias due to two-stage residual-outcome regression analysis in genetic association studies.

Science.gov (United States)

Demissie, Serkalem; Cupples, L Adrienne

2011-11-01

Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.

The Effects of Academic Experience and Prestige on Researchers’ Citing Behavior

DEFF Research Database (Denmark)

Frandsen, Tove Faber; Nicolaisen, Jeppe

2012-01-01

the number of references in their publications. Preliminary results from linear regression models suggest that two author types can be characterized using this analysis. Review experience seems to be the decisive factor in the data material. The article discusses the implications of the findings as well...

THE GENDER PAY GAP IN VIETNAM, 1993-2002: A QUANTILE REGRESSION APPROACH

OpenAIRE

Pham, Hung T; Reilly, Barry

2007-01-01

This paper uses mean and quantile regression analysis to investigate the gender pay gap for the wage employed in Vietnam over the period 1993 to 2002. It finds that the Doi moi reforms appear to have been associated with a sharp reduction in gender pay gap disparities for the wage employed. The average gender pay gap in this sector halved between 1993 and 2002 with most of the contraction evident by 1998. There has also been a narrowing in the gender pay gap at most selected points of the con...

The Gender Pay Gap In Vietnam, 1993-2002: A Quantile Regression Approach

OpenAIRE

Barry Reilly & T. Hung Pham

2006-01-01

This paper uses mean and quantile regression analysis to investigate the gender pay gap for the wage employed in Vietnam over the period 1993 to 2002. It finds that the Doi moi reforms have been associated with a sharp reduction in gender wage disparities for the wage employed. The average gender pay gap in this sector halved between 1993 and 2002 with most of the contraction evident by 1998. There has also been a contraction in the gender pay at most selected points of the conditional wage d...

Stepwise versus Hierarchical Regression: Pros and Cons

Science.gov (United States)

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

Open label smoking cessation with varenicline is associated with decreased glutamate levels and functional changes in anterior cingulate cortex: preliminary findings

Directory of Open Access Journals (Sweden)

Muriah Dawn Wheelock

2014-07-01

Full Text Available Rationale: Varenicline, the most effective single agent for smoking cessation, is a partial agonist at α4β2 nicotinic acetylcholine receptors. Increasing evidence implicates glutamate in the pathophysiology of addiction and one of the benefits of treatment for smoking cessation is the ability to regain cognitive control. Objective: To evaluate the effects of 12 week varenicline administration on glutamate levels in the dorsal anterior cingulate cortex (dACC and functional changes within the cognitive control network.Methods: We used single-voxel proton magnetic resonance spectroscopy (1H-MRS in the dACC and functional MRI (fMRI during performance of a Stroop color-naming task before and after smoking cessation with varenicline in 11 healthy smokers (open label design. Using the dACC as a seed region, we evaluated functional connectivity changes using a psychophysiological interaction (PPI analysis. Results: We observed a significant decrease in dACC glutamate + glutamine (Glx/Cr levels as well as significant blood oxygen level-dependent signal (BOLD decreases in the rostral ACC/medial orbitofrontal cortex and precuneus/posterior cingulate cortex. These BOLD changes are suggestive of alterations in default mode network (DMN function and are further supported by the results of the PPI analysis that revealed changes in connectivity between the dACC and regions of the DMN. Baseline measures of nicotine dependence and craving positively correlated with baseline Glx/Cr levels.Conclusions: These results suggest possible mechanisms of action for varenicline such as reduction in Glx levels in dACC and shifts in BOLD activities between large scale brain networks. They also suggest a role for ACC Glx in the modulation of behavior. Due to the preliminary nature of this study (lack of control group and small sample size, future studies are needed to replicate these findings.

Plutonium Immobilization Can Loading Preliminary Specifications

Energy Technology Data Exchange (ETDEWEB)

Kriikku, E.

1998-11-25

This report discusses the Plutonium Immobilization can loading preliminary equipment specifications and includes a process block diagram, process description, equipment list, preliminary equipment specifications, plan and elevation sketches, and some commercial catalogs. This report identifies loading pucks into cans and backfilling cans with helium as the top priority can loading development areas.

4. Preliminary Findings of a Prospective Study of FDG-PET in Patients with Possible Lung Cancer

DEFF Research Database (Denmark)

Mortensen; Enevoldsen; Friberg

2000-01-01

Purpose: To examine the value of PET in diagnosis and staging of suspected lung cancer.Methods: 20 (13 male; mean age: 56 yr., range: 22-83 yr.) patients with chest X-ray findings suspicious of malignancy were staged a) "clinically" (X-ray, history/physical examination, lung function), b) by chest......%) patients surgery was avoided mainly because of the PET findings. In one SCLC patient and one lymphoma patient, PET showed extensive disease, which changed the chemotherapy regime. Accuracy was 83% for clinical stage, 79% for CT and 77% for PET. Four (20%) false positive PET findings were caused...

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.

Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

Science.gov (United States)

Koon, Sharon; Petscher, Yaacov

2015-01-01

The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…

On weighted and locally polynomial directional quantile regression

Czech Academy of Sciences Publication Activity Database

Boček, Pavel; Šiman, Miroslav

2017-01-01

Roč. 32, č. 3 (2017), s. 929-946 ISSN 0943-4062 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : Quantile regression * Nonparametric regression * Nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.434, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0458380.pdf

Regression Benchmarking: An Approach to Quality Assurance in Performance

OpenAIRE

Bulej, Lubomír

2005-01-01

The paper presents a short summary of our work in the area of regression benchmarking and its application to software development. Specially, we explain the concept of regression benchmarking, the requirements for employing regression testing in a software project, and methods used for analyzing the vast amounts of data resulting from repeated benchmarking. We present the application of regression benchmarking on a real software project and conclude with a glimpse at the challenges for the fu...

Nonlinear Regression with R

CERN Document Server

Ritz, Christian; Parmigiani, Giovanni

2009-01-01

R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.

Bounded Gaussian process regression

DEFF Research Database (Denmark)

Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan

2013-01-01

We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....

There is No Quantum Regression Theorem

International Nuclear Information System (INIS)

Ford, G.W.; OConnell, R.F.

1996-01-01

The Onsager regression hypothesis states that the regression of fluctuations is governed by macroscopic equations describing the approach to equilibrium. It is here asserted that this hypothesis fails in the quantum case. This is shown first by explicit calculation for the example of quantum Brownian motion of an oscillator and then in general from the fluctuation-dissipation theorem. It is asserted that the correct generalization of the Onsager hypothesis is the fluctuation-dissipation theorem. copyright 1996 The American Physical Society

Two Paradoxes in Linear Regression Analysis

Science.gov (United States)

FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

2016-01-01

Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

A Meta-Synthesis of Qualitative Findings About Dance/Movement Therapy for Individuals With Trauma.

Science.gov (United States)

Levine, Brooklyn; Land, Helen M

2016-02-01

The therapeutic potential of using dance/movement therapy is being increasingly recognized. Preliminary interdisciplinary research findings suggest engaging the body in trauma treatment might reduce the length of treatment by addressing the connections among thoughts, feelings, neurobiology, and somatic responses in the survivor. Unfortunately, empirical research investigating its effectiveness as a psychotherapeutic intervention has been limited due to the lack of a clear manual for mental health care practitioners. The present study aims to synthesize findings from the existing qualitative literature in a qualitative meta-synthesis. Our findings will contribute to the development of a body-oriented intervention for mental health care practitioners to use for trauma. © The Author(s) 2015.

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.

Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing

NARCIS (Netherlands)

Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.

2006-01-01

The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval

Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

Science.gov (United States)

Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

2012-01-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

Monetary Policy Committee and Monetary Policy Conduct in Nigeria: A Preliminary Investigation

OpenAIRE

Ekor, Maxwell; Saka, Jimoh; Adeniyi, Oluwatosin

2014-01-01

The study provides an incisive but preliminary investigation into the activities of the monetary policy committee of the central bank of Nigeria and the implications for monetary policy, using the standard deviation measure of volatility and the ordinary least square method. The findings show that the ‘internal’ members and majority of the ‘external’ members have different preferences as shown in the voting patterns. Also, there has been reduction in inflation, money and stock markets vola...

Mixed-effects regression models in linguistics

CERN Document Server

Heylen, Kris; Geeraerts, Dirk

2018-01-01

When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed.  In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...

LOGISTIC REGRESSION AS A TOOL FOR DETERMINATION OF THE PROBABILITY OF DEFAULT FOR ENTERPRISES

Directory of Open Access Journals (Sweden)

Erika SPUCHLAKOVA

2017-12-01

Full Text Available In a rapidly changing world it is necessary to adapt to new conditions. From a day to day approaches can vary. For the proper management of the company it is essential to know the financial situation. Assessment of the company financial health can be carried out by financial analysis which provides a number of methods how to evaluate the company financial health. Analysis indicators are often included in the company assessment, in obtaining bank loans and other financial resources to ensure the functioning of the company. As company focuses on the future and its planning, it is essential to forecast the future financial situation. According to the results of company´s financial health prediction, the company decides on the extension or limitation of its business. It depends mainly on the capabilities of company´s management how they will use information obtained from financial analysis in practice. The findings of logistic regression methods were published firstly in the 60s, as an alternative to the least squares method. The essence of logistic regression is to determine the relationship between being explained (dependent variable and explanatory (independent variables. The basic principle of this static method is based on the regression analysis, but unlike linear regression, it can predict the probability of a phenomenon that has occurred or not. The aim of this paper is to determine the probability of bankruptcy enterprises.

Principles of Quantile Regression and an Application

Science.gov (United States)

Chen, Fang; Chalhoub-Deville, Micheline

2014-01-01

Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…

Utilizing the Quantile Regression to Explore the Determinants on the Application of E-Learning

OpenAIRE

Quang Linh Huynh; Thuy Lan Le Thi

2014-01-01

In this research, the quantile regression is applied to investigate the affecting factors associated with the application of e-learning. The findings provide a comprehensive picture about the relationships between the application of e-learning and its determinants. It sheds light on these complicated relationships that, at the different quantiles of the conditional distribution of e-learning adopting levels, the influence of the determinants on the application of e-learning is different. More...

The metallic finds from Çatalhöyük: a review and preliminary new work

DEFF Research Database (Denmark)

Birch, Thomas; Rehren, Thilo; Pernicka, Ernst

2013-01-01

finds from Levels South M-O has been dated to c.6600–6450 BC. Despite receiving a great deal of attention, very little research has been conducted on these finds (Neuninger et al. 1964; Sperl 1990). Starting a new approach, three Neolithic copper-based artifacts from recent excavations were selected......The metallic artifacts from Çatalhöyük are of particular importance as they constitute some of the earliest examples known. Metal finds have been recovered from as early as Level IX (South K), spanning to Level II, with VII and VI (South M-O) being the most productive (Mellaart 1964, 111...

Functional data analysis of generalized regression quantiles

KAUST Repository

Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl

2013-01-01

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

Functional data analysis of generalized regression quantiles

KAUST Repository

Guo, Mengmeng

2013-11-05

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

Preliminary result in patients with primary hepatoma treated by stereotactic radiotherapy

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Kang, Ki Mun; Choi, Ihl Bohng; Kim, In Ah; Choi, Byung Ock; Kang, Young Nam; Han, Sung Tae; Chung, Gyu Won [College of Medicine, Catholic Univ., Seoul (Korea, Republic of); Chai, Gyu Young [College of Medicine, Gyeongsang National Univ., Chinju (Korea, Republic of)

2001-03-01

It is not common to evaluate the response of the fractionated stereotactic radiotherapy (SRT) to primary hepatoma as compared with conventional radiotherapy. The purpose of the study was to take the preliminary result on the clinical trial of primary hepatoma by SRT. From July 1999 to March 2000, thirty three patients were hospitalized in the St. Mary's Hospital, and treated with SRT for extracranial tumors. Among them, 13 patients were diagnosed to primary hepatoma and then applied by frameless SRT using 6 MV linac accelerator. There were 12 male and 1 female patients. They had the age of 44-66 year old (median: 59) and the tumor size of 10-825 cc (median: 185 cc). SRT was given to them 3-5 fractions a week (5 Gy/fraction, 90% isodose line) for 2-3 weeks. Median dose of SRT was 50 Gy and the range was 30-50 Gy. Follow-up period ranged from 3 months to 13 months with median of 8 months. After treating SRT to thirteen patients with primary hepatoma, the response of the tumor was examined by abdominal CT: they are classified by 1 complete regression (7.7%), 7 partial regression (53.8%), 4 minimal regression (30.8%), 1 stable disease (7.7%). The positive responses more than partial remission were 8 patients (61.5%) after the treatment. The level of serum alpha-fetoprotein (AFP) after the treatment as compared with pretreatment had been 92.3% decreased. There was no severe complication except dyspepsia 84.6%, mild nausea 69.2%, transient decreased of hepatic function 15.4% and fever 7.7%. SRT to the patients with primary hepatoma was potentially suggested to become the safe and more effective tool than the conventional radiotherapy even though there were relatively short duration of follow-up and small numbers to be tested.

Association between biomarkers and clinical characteristics in chronic subdural hematoma patients assessed with lasso regression.

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Are Hugo Pripp

Full Text Available Chronic subdural hematoma (CSDH is characterized by an "old" encapsulated collection of blood and blood breakdown products between the brain and its outermost covering (the dura. Recognized risk factors for development of CSDH are head injury, old age and using anticoagulation medication, but its underlying pathophysiological processes are still unclear. It is assumed that a complex local process of interrelated mechanisms including inflammation, neomembrane formation, angiogenesis and fibrinolysis could be related to its development and propagation. However, the association between the biomarkers of inflammation and angiogenesis, and the clinical and radiological characteristics of CSDH patients, need further investigation. The high number of biomarkers compared to the number of observations, the correlation between biomarkers, missing data and skewed distributions may limit the usefulness of classical statistical methods. We therefore explored lasso regression to assess the association between 30 biomarkers of inflammation and angiogenesis at the site of lesions, and selected clinical and radiological characteristics in a cohort of 93 patients. Lasso regression performs both variable selection and regularization to improve the predictive accuracy and interpretability of the statistical model. The results from the lasso regression showed analysis exhibited lack of robust statistical association between the biomarkers in hematoma fluid with age, gender, brain infarct, neurological deficiencies and volume of hematoma. However, there were associations between several of the biomarkers with postoperative recurrence requiring reoperation. The statistical analysis with lasso regression supported previous findings that the immunological characteristics of CSDH are local. The relationship between biomarkers, the radiological appearance of lesions and recurrence requiring reoperation have been inclusive using classical statistical methods on these data

Simple and multiple linear regression: sample size considerations.

Science.gov (United States)

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

Removing Malmquist bias from linear regressions

Science.gov (United States)

Verter, Frances

1993-01-01

Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.

Regression relation for pure quantum states and its implications for efficient computing.

Science.gov (United States)

Elsayed, Tarek A; Fine, Boris V

2013-02-15

We obtain a modified version of the Onsager regression relation for the expectation values of quantum-mechanical operators in pure quantum states of isolated many-body quantum systems. We use the insights gained from this relation to show that high-temperature time correlation functions in many-body quantum systems can be controllably computed without complete diagonalization of the Hamiltonians, using instead the direct integration of the Schrödinger equation for randomly sampled pure states. This method is also applicable to quantum quenches and other situations describable by time-dependent many-body Hamiltonians. The method implies exponential reduction of the computer memory requirement in comparison with the complete diagonalization. We illustrate the method by numerically computing infinite-temperature correlation functions for translationally invariant Heisenberg chains of up to 29 spins 1/2. Thereby, we also test the spin diffusion hypothesis and find it in a satisfactory agreement with the numerical results. Both the derivation of the modified regression relation and the justification of the computational method are based on the notion of quantum typicality.

The microcomputer scientific software series 2: general linear model--regression.

Science.gov (United States)

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

Science.gov (United States)

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Learning a Nonnegative Sparse Graph for Linear Regression.

Science.gov (United States)

Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

2015-09-01

Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.