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

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...

Electronics a systems approach

CERN Document Server

Storey, Neil

2017-01-01

Electronics plays a central role in our everyday lives. It is at the heart of almost all of today's essential technology, from mobile phones to computers and from cars to power stations. As such, all engineers, scientists and technologists need to have a fundamental understanding of this exciting subject, and for many this will just be the beginning. Now in its sixth edition, Electronics: A Systems Approach provides an outstanding introduction to this fast-moving and important field. Comprehensively revised and updated to cover the latest developments in the world of electronics, the text continues to use Neil Storey's established and well-respected systems approach. It introduces the basic concepts first before progressing to a more advanced analysis, enabling you to contextualise what a system is designed to achieve before tackling the intricacies of designing or analysing its various components with confidence. This book is accompanied by a website which contains over 100 video tutorials to help explain ke...

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

The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations.

Science.gov (United States)

Liu, Chunping; Laporte, Audrey; Ferguson, Brian S

2008-09-01

In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital - or nursing home - up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). Productivity researchers tend to have very strong preferences over which methodology to use for efficiency estimation. In this paper, we use Monte Carlo simulation to compare the performance of DEA and SFA in terms of their ability to accurately estimate efficiency. We also evaluate quantile regression as a potential alternative approach. A Cobb-Douglas production function, random error terms and a technical inefficiency term with different distributions are used to calculate the observed output. The results, based on these experiments, suggest that neither DEA nor SFA can be regarded as clearly dominant, and that, depending on the quantile estimated, the quantile regression approach may be a useful addition to the armamentarium of methods for estimating technical efficiency.

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

Directory of Open Access Journals (Sweden)

Hsin-Lun Wu

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

Poisson regression approach for modeling fatal injury rates amongst Malaysian workers

International Nuclear Information System (INIS)

Kamarulzaman Ibrahim; Heng Khai Theng

2005-01-01

Many safety studies are based on the analysis carried out on injury surveillance data. The injury surveillance data gathered for the analysis include information on number of employees at risk of injury in each of several strata where the strata are defined in terms of a series of important predictor variables. Further insight into the relationship between fatal injury rates and predictor variables may be obtained by the poisson regression approach. Poisson regression is widely used in analyzing count data. In this study, poisson regression is used to model the relationship between fatal injury rates and predictor variables which are year (1995-2002), gender, recording system and industry type. Data for the analysis were obtained from PERKESO and Jabatan Perangkaan Malaysia. It is found that the assumption that the data follow poisson distribution has been violated. After correction for the problem of over dispersion, the predictor variables that are found to be significant in the model are gender, system of recording, industry type, two interaction effects (interaction between recording system and industry type and between year and industry type). Introduction Regression analysis is one of the most popular

Does intense monitoring matter? A quantile regression approach

Directory of Open Access Journals (Sweden)

Fekri Ali Shawtari

2017-06-01

Full Text Available Corporate governance has become a centre of attention in corporate management at both micro and macro levels due to adverse consequences and repercussion of insufficient accountability. In this study, we include the Malaysian stock market as sample to explore the impact of intense monitoring on the relationship between intellectual capital performance and market valuation. The objectives of the paper are threefold: i to investigate whether intense monitoring affects the intellectual capital performance of listed companies; ii to explore the impact of intense monitoring on firm value; iii to examine the extent to which the directors serving more than two board committees affects the linkage between intellectual capital performance and firms' value. We employ two approaches, namely, the Ordinary Least Square (OLS and the quantile regression approach. The purpose of the latter is to estimate and generate inference about conditional quantile functions. This method is useful when the conditional distribution does not have a standard shape such as an asymmetric, fat-tailed, or truncated distribution. In terms of variables, the intellectual capital is measured using the value added intellectual coefficient (VAIC, while the market valuation is proxied by firm's market capitalization. The findings of the quantile regression shows that some of the results do not coincide with the results of OLS. We found that intensity of monitoring does not influence the intellectual capital of all firms. It is also evident that intensity of monitoring does not influence the market valuation. However, to some extent, it moderates the relationship between intellectual capital performance and market valuation. This paper contributes to the existing literature as it presents new empirical evidences on the moderating effects of the intensity of monitoring of the board committees on the relationship between performance and intellectual capital.

Regression analysis with categorized regression calibrated exposure: some interesting findings

Directory of Open Access Journals (Sweden)

Hjartåker Anette

2006-07-01

Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

Directory of Open Access Journals (Sweden)

Baofeng Shi

2015-01-01

Full Text Available We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

Science.gov (United States)

Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun

2013-10-01

Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.

Analysing inequalities in Germany a structured additive distributional regression approach

CERN Document Server

Silbersdorff, Alexander

2017-01-01

This book seeks new perspectives on the growing inequalities that our societies face, putting forward Structured Additive Distributional Regression as a means of statistical analysis that circumvents the common problem of analytical reduction to simple point estimators. This new approach allows the observed discrepancy between the individuals’ realities and the abstract representation of those realities to be explicitly taken into consideration using the arithmetic mean alone. In turn, the method is applied to the question of economic inequality in Germany.

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

Science.gov (United States)

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

2011-10-15

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

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.

An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records.

Science.gov (United States)

Schildcrout, Jonathan S; Basford, Melissa A; Pulley, Jill M; Masys, Daniel R; Roden, Dan M; Wang, Deede; Chute, Christopher G; Kullo, Iftikhar J; Carrell, David; Peissig, Peggy; Kho, Abel; Denny, Joshua C

2010-12-01

We describe a two-stage analytical approach for characterizing morbidity profile dissimilarity among patient cohorts using electronic medical records. We capture morbidities using the International Statistical Classification of Diseases and Related Health Problems (ICD-9) codes. In the first stage of the approach separate logistic regression analyses for ICD-9 sections (e.g., "hypertensive disease" or "appendicitis") are conducted, and the odds ratios that describe adjusted differences in prevalence between two cohorts are displayed graphically. In the second stage, the results from ICD-9 section analyses are combined into a general morbidity dissimilarity index (MDI). For illustration, we examine nine cohorts of patients representing six phenotypes (or controls) derived from five institutions, each a participant in the electronic MEdical REcords and GEnomics (eMERGE) network. The phenotypes studied include type II diabetes and type II diabetes controls, peripheral arterial disease and peripheral arterial disease controls, normal cardiac conduction as measured by electrocardiography, and senile cataracts. Copyright © 2010 Elsevier Inc. All rights reserved.

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.

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.

A linear algebraic approach to electron-molecule collisions

International Nuclear Information System (INIS)

Collins, L.A.; Schnieder, B.I.

1982-01-01

The linear algebraic approach to electron-molecule collisions is examined by firstly deriving the general set of coupled integrodifferential equations that describe electron collisional processes and then describing the linear algebraic approach for obtaining a solution to the coupled equations. Application of the linear algebraic method to static-exchange, separable exchange and effective optical potential, is examined. (U.K.)

A multi-scale relevance vector regression approach for daily urban water demand forecasting

Science.gov (United States)

Bai, Yun; Wang, Pu; Li, Chuan; Xie, Jingjing; Wang, Yin

2014-09-01

Water is one of the most important resources for economic and social developments. Daily water demand forecasting is an effective measure for scheduling urban water facilities. This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand. The approach uses the stationary wavelet transform to decompose historical time series of daily water supplies into different scales. At each scale, the wavelet coefficients are used to train a machine-learning model using the relevance vector regression (RVR) method. The estimated coefficients of the RVR outputs for all of the scales are employed to reconstruct the forecasting result through the inverse wavelet transform. To better facilitate the MSRVR forecasting, the chaos features of the daily water supply series are analyzed to determine the input variables of the RVR model. In addition, an adaptive chaos particle swarm optimization algorithm is used to find the optimal combination of the RVR model parameters. The MSRVR approach is evaluated using real data collected from two waterworks and is compared with recently reported methods. The results show that the proposed MSRVR method can forecast daily urban water demand much more precisely in terms of the normalized root-mean-square error, correlation coefficient, and mean absolute percentage error criteria.

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.

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.

Science.gov (United States)

Yu, Wenbao; Park, Taesung

2014-01-01

It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes. We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

DEFF Research Database (Denmark)

Schlechtingen, Meik; Santos, Ilmar

2011-01-01

This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal...

Comparison of beta-binomial regression model approaches to analyze health-related quality of life data.

Science.gov (United States)

Najera-Zuloaga, Josu; Lee, Dae-Jin; Arostegui, Inmaculada

2017-01-01

Health-related quality of life has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of health-related quality of life with patients and disease characteristics has become one of the primary aims of many health-related quality of life studies. Health-related quality of life scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data; however, the beta-binomial regression has been performed by means of two different approaches in the literature: (i) beta-binomial distribution with a logistic link; and (ii) hierarchical generalized linear models. None of the existing literature in the analysis of health-related quality of life survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of health-related quality of life outcomes in patients with Chronic Obstructive Pulmonary Disease, where the use of both approaches yields to contradictory results in terms of covariate effects significance and consequently the interpretation of the most relevant factors in health-related quality of life. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of health-related quality of life survey data for practitioners, providing an R package.

Two-process approach to electron beam welding control

International Nuclear Information System (INIS)

Lastovirya, V.N.

1987-01-01

The analysis and synthesis of multi-dimensional welding control systems, which require the usage of computers, should be conducted within the temporal range. From the general control theory point two approaches - one-process and two-process - are possible to electron beam welding. In case of two-process approach, subprocesses of heat source formation and direct metal melting are separated. Two-process approach leads to two-profile control system and provides the complete controlability of electron beam welding within the frameworks of systems with concentrated, as well as, with distributed parameters. Approach choice for the given problem solution is determined, first of all, by stability degree of heat source during welding

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...

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...

Modelling the return distribution of salmon farming companies : a quantile regression approach

OpenAIRE

Jacobsen, Fredrik

2017-01-01

The salmon farming industry has gained increased attention from investors, portfolio managers, financial analysts and other stakeholders the recent years. Despite this development, very little is known about the risk and return of salmon farming company stocks, and especially how the relationship between risk and return varies under different market conditions, given the volatile nature of the salmon farming industry. We approach this problem by using quantile regression to examine the relati...

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

Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.

Science.gov (United States)

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.

Effective approaches for managing electronic records and archives

CERN Document Server

Dearstyne, Bruce W

2006-01-01

This is a book of fresh insights, perspectives, strategies, and approaches for managing electronic records and archives. The authors draw on first-hand experience to present practical solutions, including recommendations for building and sustaining strong electronic records programs.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

Science.gov (United States)

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction

International Nuclear Information System (INIS)

Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.

2013-01-01

Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability

A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

Energy Technology Data Exchange (ETDEWEB)

Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)

2012-02-01

New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

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

International Nuclear Information System (INIS)

Baser, Furkan; Demirhan, Haydar

2017-01-01

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

A different approach to estimate nonlinear regression model using numerical methods

Science.gov (United States)

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

2017-11-01

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

Electron momentum density and Compton profile by a semi-empirical approach

Science.gov (United States)

Aguiar, Julio C.; Mitnik, Darío; Di Rocco, Héctor O.

2015-08-01

Here we propose a semi-empirical approach to describe with good accuracy the electron momentum densities and Compton profiles for a wide range of pure crystalline metals. In the present approach, we use an experimental Compton profile to fit an analytical expression for the momentum densities of the valence electrons. This expression is similar to a Fermi-Dirac distribution function with two parameters, one of which coincides with the ground state kinetic energy of the free-electron gas and the other resembles the electron-electron interaction energy. In the proposed scheme conduction electrons are neither completely free nor completely bound to the atomic nucleus. This procedure allows us to include correlation effects. We tested the approach for all metals with Z=3-50 and showed the results for three representative elements: Li, Be and Al from high-resolution experiments.

NASA and COTS Electronics: Past Approach and Successes - Future Considerations

Science.gov (United States)

LaBel, Kenneth A.

2018-01-01

NASA has a long history of using commercial grade electronics in space. In this talk, a brief history of NASAâ's trends and approaches to commercial grade electronics focusing on processing and memory systems will be presented. This will include providing summary information on the space hazards to electronics as well as NASA mission trade space. We will also discuss developing recommendations for risk management approaches to Electrical, Electronic and Electromechanical (EEE) parts and reliability in space. The final portion of the talk will discuss emerging aerospace trends and the future for Commercial Off The Shelf (COTS) usage.

A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies

Directory of Open Access Journals (Sweden)

Jingyuan Zhao

2012-01-01

Full Text Available We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by using L1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD penalty (Fan and Li, 2001 and Jeffrey’s Prior penalty (Firth, 1993, a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008. The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005 and the LASSO-patternsearch algorithm (Shi et al. 2007.

Hydrodynamic approach to electronic transport in graphene

Energy Technology Data Exchange (ETDEWEB)

Narozhny, Boris N. [Institute for Theoretical Condensed Matter Physics, Karlsruhe Institute of Technology, Karlsruhe (Germany); National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow (Russian Federation); Gornyi, Igor V. [Institute for Theoretical Condensed Matter Physics, Karlsruhe Institute of Technology, Karlsruhe (Germany); Institute of Nanotechnology, Karlsruhe Institute of Technology, Karlsruhe (Germany); Ioffe Physical Technical Institute, St. Petersburg (Russian Federation); Mirlin, Alexander D. [Institute for Theoretical Condensed Matter Physics, Karlsruhe Institute of Technology, Karlsruhe (Germany); Institute of Nanotechnology, Karlsruhe Institute of Technology, Karlsruhe (Germany); Petersburg Nuclear Physics Institute, St. Petersburg (Russian Federation); Schmalian, Joerg [Institute for Theoretical Condensed Matter Physics, Karlsruhe Institute of Technology, Karlsruhe (Germany); Institute for Solid State Physics, Karlsruhe Institute of Technology, Karlsruhe (Germany)

2017-11-15

The last few years have seen an explosion of interest in hydrodynamic effects in interacting electron systems in ultra-pure materials. In this paper we briefly review the recent advances, both theoretical and experimental, in the hydrodynamic approach to electronic transport in graphene, focusing on viscous phenomena, Coulomb drag, non-local transport measurements, and possibilities for observing nonlinear effects. (copyright 2017 by WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

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.

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.

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

Science.gov (United States)

Lusiana, Evellin Dewi

2017-12-01

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

The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity.

Science.gov (United States)

Hallquist, Michael N; Hwang, Kai; Luna, Beatriz

2013-11-15

Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass-regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10-.35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass-regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability. Copyright © 2013 Elsevier Inc. All rights reserved.

A multi-frequency approach to free electron lasers driven by short electron bunches

International Nuclear Information System (INIS)

Piovella, Nicola

1997-01-01

A multi-frequency model for free electron lasers (FELs), based on the Fourier decomposition of the radiation field coupled with the beam electrons, is discussed. We show that the multi-frequency approach allows for an accurate description of the evolution of the radiation spectrum, also when the FEL is driven by short electron bunches, of arbitrary longitudinal profile. We derive from the multi-frequency model, by averaging over one radiation period, the usual FEL equations modelling the slippage between radiation and particles and describing the super-radiant regime in high-gain FELs. As an example of application of the multi-frequency model, we discuss the coherent spontaneous emission (CSE) from short electron bunches

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.)

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.

Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

International Nuclear Information System (INIS)

Chen, Kuilin; Yu, Jie

2014-01-01

Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations

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…

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.

Integrated Nationwide Electronic Health Records system: Semi-distributed architecture approach.

Science.gov (United States)

Fragidis, Leonidas L; Chatzoglou, Prodromos D; Aggelidis, Vassilios P

2016-11-14

The integration of heterogeneous electronic health records systems by building an interoperable nationwide electronic health record system provides undisputable benefits in health care, like superior health information quality, medical errors prevention and cost saving. This paper proposes a semi-distributed system architecture approach for an integrated national electronic health record system incorporating the advantages of the two dominant approaches, the centralized architecture and the distributed architecture. The high level design of the main elements for the proposed architecture is provided along with diagrams of execution and operation and data synchronization architecture for the proposed solution. The proposed approach effectively handles issues related to redundancy, consistency, security, privacy, availability, load balancing, maintainability, complexity and interoperability of citizen's health data. The proposed semi-distributed architecture offers a robust interoperability framework without healthcare providers to change their local EHR systems. It is a pragmatic approach taking into account the characteristics of the Greek national healthcare system along with the national public administration data communication network infrastructure, for achieving EHR integration with acceptable implementation cost.

A Practical pedestrian approach to parsimonious regression with inaccurate inputs

Directory of Open Access Journals (Sweden)

Seppo Karrila

2014-04-01

Full Text Available A measurement result often dictates an interval containing the correct value. Interval data is also created by roundoff, truncation, and binning. We focus on such common interval uncertainty in data. Inaccuracy in model inputs is typically ignored on model fitting. We provide a practical approach for regression with inaccurate data: the mathematics is easy, and the linear programming formulations simple to use even in a spreadsheet. This self-contained elementary presentation introduces interval linear systems and requires only basic knowledge of algebra. Feature selection is automatic; but can be controlled to find only a few most relevant inputs; and joint feature selection is enabled for multiple modeled outputs. With more features than cases, a novel connection to compressed sensing emerges: robustness against interval errors-in-variables implies model parsimony, and the input inaccuracies determine the regularization term. A small numerical example highlights counterintuitive results and a dramatic difference to total least squares.

Grid-based electronic structure calculations: The tensor decomposition approach

Energy Technology Data Exchange (ETDEWEB)

Rakhuba, M.V., E-mail: rakhuba.m@gmail.com [Skolkovo Institute of Science and Technology, Novaya St. 100, 143025 Skolkovo, Moscow Region (Russian Federation); Oseledets, I.V., E-mail: i.oseledets@skoltech.ru [Skolkovo Institute of Science and Technology, Novaya St. 100, 143025 Skolkovo, Moscow Region (Russian Federation); Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina St. 8, 119333 Moscow (Russian Federation)

2016-05-01

We present a fully grid-based approach for solving Hartree–Fock and all-electron Kohn–Sham equations based on low-rank approximation of three-dimensional electron orbitals. Due to the low-rank structure the total complexity of the algorithm depends linearly with respect to the one-dimensional grid size. Linear complexity allows for the usage of fine grids, e.g. 8192{sup 3} and, thus, cheap extrapolation procedure. We test the proposed approach on closed-shell atoms up to the argon, several molecules and clusters of hydrogen atoms. All tests show systematical convergence with the required accuracy.

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,

An Introduction to the Hybrid Approach of Neural Networks and the Linear Regression Model : An Illustration in the Hedonic Pricing Model of Building Costs

OpenAIRE

浅野, 美代子; マーコ, ユー K.W.

2007-01-01

This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is pr...

Partitioning of late gestation energy expenditure in ewes using indirect calorimetry and a linear regression approach

DEFF Research Database (Denmark)

Kiani, Alishir; Chwalibog, André; Nielsen, Mette O

2007-01-01

Late gestation energy expenditure (EE(gest)) originates from energy expenditure (EE) of development of conceptus (EE(conceptus)) and EE of homeorhetic adaptation of metabolism (EE(homeorhetic)). Even though EE(gest) is relatively easy to quantify, its partitioning is problematic. In the present...... study metabolizable energy (ME) intake ranges for twin-bearing ewes were 220-440, 350- 700, 350-900 kJ per metabolic body weight (W0.75) at week seven, five, two pre-partum respectively. Indirect calorimetry and a linear regression approach were used to quantify EE(gest) and then partition to EE......(conceptus) and EE(homeorhetic). Energy expenditure of basal metabolism of the non-gravid tissues (EE(bmng)), derived from the intercept of the linear regression equation of retained energy [kJ/W0.75] and ME intake [kJ/W(0.75)], was 298 [kJ/ W0.75]. Values of the intercepts of the regression equations at week seven...

Neo-Institutional Approach to the Study of Electronic Government

Directory of Open Access Journals (Sweden)

Yan I. Vaslavskiy

2016-01-01

Full Text Available The article is devoted to the neo-institutional approach as a methodological basis in the study of electronic government. In this article substantiates the choice of neo-institutional approach to the study of the processes of implementation of information and communication technologies in the activity of state institutions, analyzes the differences of neoinstitutionalism from traditional institutional approach, considers the features of the different directions of neo-institutionalism, namely sociological, historical and rational choice theory. Attention is paid to the reasons for the renewed interest in political institutions in political science. The article emphasizes the importance of considering the electronic government as an institution, and the conditions for its implementation in the Russian political system as the institutional environment. The authors pay special attention to the variety of sociological neo-institutionalism, used, in addition to political science in sociology of organizations. The article substantiates the value of using sociological institutionalism to explore the electronic government based on a comparative analysis of e-government projects in Russia and abroad and explores its heuristic capabilities. It examines the impact of the system of norms and values of the institutional environment on the processes of formation and development of electronic government in Russia. The research capacity of this theory is due to the fact that it allows us to trace the reasons for copying and replication of inefficient practices and organizational and management schemes, to identify the factors impeding innovation use by the state of electronic interaction technologies. It is emphasized that the use of the theory of institutional isomorphism is useful in the sphere of implementation of electronic technologies, in which a key role play pluralism, horizontal managerial communication, inter-agency coordination.

A regression approach for Zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

Yung Liu, Y.; Bement, A.L.

1977-01-01

In this paper the methodology of multiple regressions as applied to Zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor Zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) When there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets. (2) Regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections. Multiple regression analysis performed on a set of carefully selected Zircaloy-2 in-reactor creep data leads to a model which provides excellent correlations for the data. (Auth.)

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.

Many-electron approaches in physics, chemistry and mathematics a multidisciplinary view

CERN Document Server

Site, Luigi

2014-01-01

This book provides a broad description of the development and (computational) application of many-electron approaches from a multidisciplinary perspective. In the context of studying many-electron systems Computer Science, Chemistry, Mathematics and Physics are all intimately interconnected. However, beyond a handful of communities working at the interface between these disciplines, there is still a marked separation of subjects. This book seeks to offer a common platform for possible exchanges between the various fields and to introduce the reader to perspectives for potential further developments across the disciplines. The rapid advances of modern technology will inevitably require substantial improvements in the approaches currently used, which will in turn make exchanges between disciplines indispensable. In essence this book is one of the very first attempts at an interdisciplinary approach to the many-electron problem.

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

Science.gov (United States)

Liu, Fang; Eugenio, Evercita C

2018-04-01

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

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.

Toward Environmentally Robust Organic Electronics: Approaches and Applications.

Science.gov (United States)

Lee, Eun Kwang; Lee, Moo Yeol; Park, Cheol Hee; Lee, Hae Rang; Oh, Joon Hak

2017-11-01

Recent interest in flexible electronics has led to a paradigm shift in consumer electronics, and the emergent development of stretchable and wearable electronics is opening a new spectrum of ubiquitous applications for electronics. Organic electronic materials, such as π-conjugated small molecules and polymers, are highly suitable for use in low-cost wearable electronic devices, and their charge-carrier mobilities have now exceeded that of amorphous silicon. However, their commercialization is minimal, mainly because of weaknesses in terms of operational stability, long-term stability under ambient conditions, and chemical stability related to fabrication processes. Recently, however, many attempts have been made to overcome such instabilities of organic electronic materials. Here, an overview is provided of the strategies developed for environmentally robust organic electronics to overcome the detrimental effects of various critical factors such as oxygen, water, chemicals, heat, and light. Additionally, molecular design approaches to π-conjugated small molecules and polymers that are highly stable under ambient and harsh conditions are explored; such materials will circumvent the need for encapsulation and provide a greater degree of freedom using simple solution-based device-fabrication techniques. Applications that are made possible through these strategies are highlighted. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linear regression crash prediction models : issues and proposed solutions.

Science.gov (United States)

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Single-electron multiplication statistics as a combination of Poissonian pulse height distributions using constraint regression methods

International Nuclear Information System (INIS)

Ballini, J.-P.; Cazes, P.; Turpin, P.-Y.

1976-01-01

Analysing the histogram of anode pulse amplitudes allows a discussion of the hypothesis that has been proposed to account for the statistical processes of secondary multiplication in a photomultiplier. In an earlier work, good agreement was obtained between experimental and reconstructed spectra, assuming a first dynode distribution including two Poisson distributions of distinct mean values. This first approximation led to a search for a method which could give the weights of several Poisson distributions of distinct mean values. Three methods have been briefly exposed: classical linear regression, constraint regression (d'Esopo's method), and regression on variables subject to error. The use of these methods gives an approach of the frequency function which represents the dispersion of the punctual mean gain around the whole first dynode mean gain value. Comparison between this function and the one employed in Polya distribution allows the statement that the latter is inadequate to describe the statistical process of secondary multiplication. Numerous spectra obtained with two kinds of photomultiplier working under different physical conditions have been analysed. Then two points are discussed: - Does the frequency function represent the dynode structure and the interdynode collection process. - Is the model (the multiplication process of all dynodes but the first one, is Poissonian) valid whatever the photomultiplier and the utilization conditions. (Auth.)

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.

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Science.gov (United States)

Yang, Aiyuan; Yan, Chunxia; Zhu, Feng; Zhao, Zhongmeng; Cao, Zhi

2013-01-01

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds. PMID:23984382

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Directory of Open Access Journals (Sweden)

Xuanping Zhang

2013-01-01

Full Text Available Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR, which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds.

Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach.

Science.gov (United States)

Stollenwerk, Björn; Welchowski, Thomas; Vogl, Matthias; Stock, Stephanie

2016-04-01

Despite the increasing availability of routine data, no analysis method has yet been presented for cost-of-illness (COI) studies based on massive data. We aim, first, to present such a method and, second, to assess the relevance of the associated gain in numerical efficiency. We propose a prevalence-based, top-down regression approach consisting of five steps: aggregating the data; fitting a generalized additive model (GAM); predicting costs via the fitted GAM; comparing predicted costs between prevalent and non-prevalent subjects; and quantifying the stochastic uncertainty via error propagation. To demonstrate the method, it was applied to aggregated data in the context of chronic lung disease to German sickness funds data (from 1999), covering over 7.3 million insured. To assess the gain in numerical efficiency, the computational time of the innovative approach has been compared with corresponding GAMs applied to simulated individual-level data. Furthermore, the probability of model failure was modeled via logistic regression. Applying the innovative method was reasonably fast (19 min). In contrast, regarding patient-level data, computational time increased disproportionately by sample size. Furthermore, using patient-level data was accompanied by a substantial risk of model failure (about 80 % for 6 million subjects). The gain in computational efficiency of the innovative COI method seems to be of practical relevance. Furthermore, it may yield more precise cost estimates.

Alternative regression models to assess increase in childhood BMI

OpenAIRE

Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

2008-01-01

Abstract Background Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 childre...

Electronic structure of a striped nickelate studied by the exact exchange for correlated electrons (EECE) approach

KAUST Repository

Schwingenschlögl, Udo

2009-12-01

Motivated by a RIXS study of Wakimoto, et al.(Phys. Rev. Lett., 102 (2009) 157001) we use density functional theory to analyze the magnetic order in the nickelate La5/3Sr1/3NiO4 and the details of its crystal and electronic structure. We compare the generalized gradient approximation to the hybrid functional approach of exact exchange for correlated electrons (EECE). In contrast to the former, the latter reproduces the insulating state of the compound and the midgap states. The EECE approach, in general, appears to be appropriate for describing stripe phases in systems with orbital degrees of freedom. Copyright © EPLA, 2009.

Validity of the reduced-sample insulin modified frequently-sampled intravenous glucose tolerance test using the nonlinear regression approach.

Science.gov (United States)

Sumner, Anne E; Luercio, Marcella F; Frempong, Barbara A; Ricks, Madia; Sen, Sabyasachi; Kushner, Harvey; Tulloch-Reid, Marshall K

2009-02-01

The disposition index, the product of the insulin sensitivity index (S(I)) and the acute insulin response to glucose, is linked in African Americans to chromosome 11q. This link was determined with S(I) calculated with the nonlinear regression approach to the minimal model and data from the reduced-sample insulin-modified frequently-sampled intravenous glucose tolerance test (Reduced-Sample-IM-FSIGT). However, the application of the nonlinear regression approach to calculate S(I) using data from the Reduced-Sample-IM-FSIGT has been challenged as being not only inaccurate but also having a high failure rate in insulin-resistant subjects. Our goal was to determine the accuracy and failure rate of the Reduced-Sample-IM-FSIGT using the nonlinear regression approach to the minimal model. With S(I) from the Full-Sample-IM-FSIGT considered the standard and using the nonlinear regression approach to the minimal model, we compared the agreement between S(I) from the Full- and Reduced-Sample-IM-FSIGT protocols. One hundred African Americans (body mass index, 31.3 +/- 7.6 kg/m(2) [mean +/- SD]; range, 19.0-56.9 kg/m(2)) had FSIGTs. Glucose (0.3 g/kg) was given at baseline. Insulin was infused from 20 to 25 minutes (total insulin dose, 0.02 U/kg). For the Full-Sample-IM-FSIGT, S(I) was calculated based on the glucose and insulin samples taken at -1, 1, 2, 3, 4, 5, 6, 7, 8,10, 12, 14, 16, 19, 22, 23, 24, 25, 27, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150, and 180 minutes. For the Reduced-Sample-FSIGT, S(I) was calculated based on the time points that appear in bold. Agreement was determined by Spearman correlation, concordance, and the Bland-Altman method. In addition, for both protocols, the population was divided into tertiles of S(I). Insulin resistance was defined by the lowest tertile of S(I) from the Full-Sample-IM-FSIGT. The distribution of subjects across tertiles was compared by rank order and kappa statistic. We found that the rate of failure of resolution of S(I) by

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.

Model-free prediction and regression a transformation-based approach to inference

CERN Document Server

Politis, Dimitris N

2015-01-01

The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, co...

Orbital approach to the electronic structure of solids

CERN Document Server

Canadell, Enric; Iung, Christophe

2012-01-01

This book provides an intuitive yet sound understanding of how structure and properties of solids may be related. The natural link is provided by the band theory approach to the electronic structure of solids. The chemically insightful concept of orbital interaction and the essential machinery of band theory are used throughout the book to build links between the crystal and electronic structure of periodic systems. In such a way, it is shown how important tools for understandingproperties of solids like the density of states, the Fermi surface etc. can be qualitatively sketched and used to ei

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.

Investigating the complex relationship between in situ Southern Ocean pCO2 and its ocean physics and biogeochemical drivers using a nonparametric regression approach

CSIR Research Space (South Africa)

Pretorius, W

2014-01-01

Full Text Available the relationship more accurately in terms of MSE, RMSE and MAE, than a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict p...

A regression approach for zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

Yung Liu, Y.; Bement, A.L.

1977-01-01

In this paper the methodology of multiple regressions as applied to zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. From data analysis and model development point of views, both the assumption of independence and prior committment to specific model forms are unacceptable. One would desire means which can not only estimate the required parameters directly from data but also provide basis for model selections, viz., one model against others. Basic understanding of the physics of deformation is important in choosing the forms of starting physical model equations, but the justifications must rely on their abilities in correlating the overall data. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) when there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets, (2) regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections

Understanding electron magnetic circular dichroism in a transition potential approach

Science.gov (United States)

Barthel, J.; Mayer, J.; Rusz, J.; Ho, P.-L.; Zhong, X. Y.; Lentzen, M.; Dunin-Borkowski, R. E.; Urban, K. W.; Brown, H. G.; Findlay, S. D.; Allen, L. J.

2018-04-01

This paper introduces an approach based on transition potentials for inelastic scattering to understand the underlying physics of electron magnetic circular dichroism (EMCD). The transition potentials are sufficiently localized to permit atomic-scale EMCD. Two-beam and three-beam systematic row cases are discussed in detail in terms of transition potentials for conventional transmission electron microscopy, and the basic symmetries which arise in the three-beam case are confirmed experimentally. Atomic-scale EMCD in scanning transmission electron microscopy (STEM), using both a standard STEM probe and vortex beams, is discussed.

Exciton Scattering approach for conjugated macromolecules: from electronic spectra to electron-phonon coupling

Science.gov (United States)

Tretiak, Sergei

2014-03-01

The exciton scattering (ES) technique is a multiscale approach developed for efficient calculations of excited-state electronic structure and optical spectra in low-dimensional conjugated macromolecules. Within the ES method, the electronic excitations in the molecular structure are attributed to standing waves representing quantum quasi-particles (excitons), which reside on the graph. The exciton propagation on the linear segments is characterized by the exciton dispersion, whereas the exciton scattering on the branching centers is determined by the energy-dependent scattering matrices. Using these ES energetic parameters, the excitation energies are then found by solving a set of generalized ``particle in a box'' problems on the graph that represents the molecule. All parameters can be extracted from quantum-chemical computations of small molecular fragments and tabulated in the ES library for further applications. Subsequently, spectroscopic modeling for any macrostructure within considered molecular family could be performed with negligible numerical effort. The exciton scattering properties of molecular vertices can be further described by tight-binding or equivalently lattice models. The on-site energies and hopping constants are obtained from the exciton dispersion and scattering matrices. Such tight-binding model approach is particularly useful to describe the exciton-phonon coupling, energetic disorder and incoherent energy transfer in large branched conjugated molecules. Overall the ES applications accurately reproduce the optical spectra compared to the reference quantum chemistry results, and make possible to predict spectra of complex macromolecules, where conventional electronic structure calculations are unfeasible.

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....

Statistical approach for selection of regression model during validation of bioanalytical method

Directory of Open Access Journals (Sweden)

Natalija Nakov

2014-06-01

Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.

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

Testing for Stock Market Contagion: A Quantile Regression Approach

NARCIS (Netherlands)

S.Y. Park (Sung); W. Wang (Wendun); N. Huang (Naijing)

2015-01-01

markdownabstract__Abstract__ Regarding the asymmetric and leptokurtic behavior of financial data, we propose a new contagion test in the quantile regression framework that is robust to model misspecification. Unlike conventional correlation-based tests, the proposed quantile contagion test

On Solving Lq-Penalized Regressions

Directory of Open Access Journals (Sweden)

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.

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

Cyber Physical Systems Approach to Power Electronics Education

Directory of Open Access Journals (Sweden)

Marko Vekić

2012-12-01

Full Text Available This paper proposes a Cyber Physical Approach (CPS to power electronics (PE education where all aspects of PE technology from circuit topology to the implementation of real time control code on a microprocessor are dealt with as an inseparable whole, and only the system complexity is increased during the course of instruction. This approach is now made practical thanks to the affordable and unrestricted access to high-power PE laboratory infrastructure (PE laboratory in a box in the form of high-fidelity digital PE emulators with 1us calculation time step and latency.

Molecular self-assembly approaches for supramolecular electronic and organic electronic devices

Science.gov (United States)

Yip, Hin-Lap

Molecular self-assembly represents an efficient bottom-up strategy to generate structurally well-defined aggregates of semiconducting pi-conjugated materials. The capability of tuning the chemical structures, intermolecular interactions and nanostructures through molecular engineering and novel materials processing renders it possible to tailor a large number of unprecedented properties such as charge transport, energy transfer and light harvesting. This approach does not only benefit traditional electronic devices based on bulk materials, but also generate a new research area so called "supramolecular electronics" in which electronic devices are built up with individual supramolecular nanostructures with size in the sub-hundred nanometers range. My work combined molecular self-assembly together with several novel materials processing techniques to control the nucleation and growth of organic semiconducting nanostructures from different type of pi-conjugated materials. By tailoring the interactions between the molecules using hydrogen bonds and pi-pi stacking, semiconducting nanoplatelets and nanowires with tunable sizes can be fabricated in solution. These supramolecular nanostructures were further patterned and aligned on solid substrates through printing and chemical templating methods. The capability to control the different hierarchies of organization on surface provides an important platform to study their structural-induced electronic properties. In addition to using molecular self-assembly to create different organic nanostructures, functional self-assembled monolayer (SAM) formed by spontaneous chemisorption on surfaces was used to tune the interfacial property in organic solar cells. Devices showed dramatically improved performance when appropriate SAMs were applied to optimize the contact property for efficiency charge collection.

General approach to understanding the electronic structure of graphene on metals

International Nuclear Information System (INIS)

Voloshina, E N; Dedkov, Yu S

2014-01-01

This manuscript presents the general approach to the understanding of the connection between bonding mechanism and electronic structure of graphene on metals. To demonstrate its validity, two limiting cases of ‘weakly’ and ‘strongly’ bonded graphene on Al(111) and Ni(111) are considered, where the Dirac cone is preserved or fully destroyed, respectively. Furthermore, the electronic structure, i.e. doping level, hybridization effects, as well as a gap formation at the Dirac point of the intermediate system, graphene/Cu(111), is fully understood in the framework of the proposed approach. This work summarises the long-term debates regarding connection of the bonding strength and the valence band modification in the graphene/metal systems and paves a way for the effective control of the electronic states of graphene in the vicinity of the Fermi level. (paper)

The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard

and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...

Heterogeneous effects of oil shocks on exchange rates: evidence from a quantile regression approach.

Science.gov (United States)

Su, Xianfang; Zhu, Huiming; You, Wanhai; Ren, Yinghua

2016-01-01

The determinants of exchange rates have attracted considerable attention among researchers over the past several decades. Most studies, however, ignore the possibility that the impact of oil shocks on exchange rates could vary across the exchange rate returns distribution. We employ a quantile regression approach to address this issue. Our results indicate that the effect of oil shocks on exchange rates is heterogeneous across quantiles. A large US depreciation or appreciation tends to heighten the effects of oil shocks on exchange rate returns. Positive oil demand shocks lead to appreciation pressures in oil-exporting countries and this result is robust across lower and upper return distributions. These results offer rich and useful information for investors and decision-makers.

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.

Differentiating regressed melanoma from regressed lichenoid keratosis.

Science.gov (United States)

Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

2017-04-01

Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Quantile Regression With Measurement Error

KAUST Repository

Wei, Ying; Carroll, Raymond J.

2009-01-01

. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a

Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC, multiple criteria evaluation and Bayesian approaches

Directory of Open Access Journals (Sweden)

Shelley M. ALEXANDER

2009-02-01

Full Text Available We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS-based approaches: logistic regression and Akaike’s Information Criterion (AIC, Multiple Criteria Evaluation (MCE, and Bayesian Analysis (specifically Dempster-Shafer theory. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy, the failure to predict a species where it occurred (omission error and the prediction of presence where there was absence (commission error. Our overall accuracy showed the logistic regression approach was the most accurate (74.51%. The multiple criteria evaluation was intermediate (39.22%, while the Dempster-Shafer (D-S theory model was the poorest (29.90%. However, omission and commission error tell us a different story: logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans[Current Zoology 55(1: 28 – 40, 2009].

Path-integral approach to resonant electron-molecule scattering

International Nuclear Information System (INIS)

Winterstetter, M.; Domcke, W.

1993-01-01

A path-integral formulation of resonant electron-molecule scattering is developed within the framework of the projection-operator formalism of scattering theory. The formation and decay of resonances is treated in real time as a quantum-mechanical electronic-tunneling process, modified by the coupling of the electronic motion with the nuclear degrees of freedom. It is shown that the electronic continuum can be summed over in the path-integral formulation, resulting formally in the path integral for an effective two-state system with coupling to vibrations. The harmonic-oscillator approximation is adopted for the vibrational motion in the present work. Approximation methods are introduced which render the numerical evaluation of the sum over paths feasible for up to ∼10 3 elementary time slices. The theory is numerically realized for simple but nontrivial models representing the 2 Π g d-wave shape resonance in e - +N 2 collisions and the 2 Σ u + p-wave shape resonance in e - +H 2 collisions, respectively. The accuracy of the path-integral results is assessed by comparison with exact numerical reference data for these models. The essential virtue of the path-integral approach is the fact that the computational effort scales at most linearly with the number of vibrational degrees of freedom. The path-integral method is thus well suited to treat electron collisions with polyatomic molecules and molecular aggregates

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.

Teaching Electronic Literacy A Concepts-Based Approach for School Library Media Specialists

CERN Document Server

Craver, Kathleen W

1997-01-01

School library media specialists will find this concepts-based approach to teaching electronic literacy an indispensable basic tool for instructing students and teachers. It provides step-by-step instruction on how to find and evaluate needed information from electronic databases and the Internet, how to formulate successful electronic search strategies and retrieve relevant results, and how to interpret and critically analyze search results. The chapters contain a suggested lesson plan and sample assignments for the school library media specialist to use in teaching electronic literacy skills

Regression-based approach for testing the association between multi-region haplotype configuration and complex trait

Directory of Open Access Journals (Sweden)

Zhao Hongbo

2009-09-01

Full Text Available Abstract Background It is quite common that the genetic architecture of complex traits involves many genes and their interactions. Therefore, dealing with multiple unlinked genomic regions simultaneously is desirable. Results In this paper we develop a regression-based approach to assess the interactions of haplotypes that belong to different unlinked regions, and we use score statistics to test the null hypothesis of non-genetic association. Additionally, multiple marker combinations at each unlinked region are considered. The multiple tests are settled via the minP approach. The P value of the "best" multi-region multi-marker configuration is corrected via Monte-Carlo simulations. Through simulation studies, we assess the performance of the proposed approach and demonstrate its validity and power in testing for haplotype interaction association. Conclusion Our simulations showed that, for binary trait without covariates, our proposed methods prove to be equal and even more powerful than htr and hapcc which are part of the FAMHAP program. Additionally, our model can be applied to a wider variety of traits and allow adjustment for other covariates. To test the validity, our methods are applied to analyze the association between four unlinked candidate genes and pig meat quality.

Analytic approach to auroral electron transport and energy degradation

International Nuclear Information System (INIS)

Stamnes, K.

1980-01-01

The interaction of a beam of auroral electrons with the atmosphere is described by the linear transport equation, encompassing discrete energy loss, multiple scattering, and secondary electrons. A solution to the transport equation provides the electron intensity as a function of altitude, pitch angle (with respect to the geomagnetic field) and energy. A multi-stream (discrete ordinate) approximation to the transport equation is developed. An analytic solution is obtained in this approximation. The computational scheme obtained by combining the present transport code with the energy degradation method of Swartz (1979) conserves energy identically. The theory provides a framework within which angular distributions can be easily calculated and interpreted. Thus, a detailed study of the angular distributions of 'non-absorbed' electrons (i.e., electrons that have lost just a small fraction of their incident energy) reveals a systematic variation with incident angle and energy, and with penetration depth. The present approach also gives simple yet accurate solutions in low order multi-stream approximations. The accuracy of the four-stream approximation is generally within a few per cent, whereas two-stream results for backscattered mean intensities and fluxes are accurate to within 10-15%. (author)

A Gaussian process regression based hybrid approach for short-term wind speed prediction

International Nuclear Information System (INIS)

Zhang, Chi; Wei, Haikun; Zhao, Xin; Liu, Tianhong; Zhang, Kanjian

2016-01-01

Highlights: • A novel hybrid approach is proposed for short-term wind speed prediction. • This method combines the parametric AR model with the non-parametric GPR model. • The relative importance of different inputs is considered. • Different types of covariance functions are considered and combined. • It can provide both accurate point forecasts and satisfactory prediction intervals. - Abstract: This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian process regression (GPR) for probabilistic wind speed forecasting. In the proposed approach, the AR model is employed to capture the overall structure from wind speed series, and the GPR is adopted to extract the local structure. Additionally, automatic relevance determination (ARD) is used to take into account the relative importance of different inputs, and different types of covariance functions are combined to capture the characteristics of the data. The proposed hybrid model is compared with the persistence model, artificial neural network (ANN), and support vector machine (SVM) for one-step ahead forecasting, using wind speed data collected from three wind farms in China. The forecasting results indicate that the proposed method can not only improve point forecasts compared with other methods, but also generate satisfactory prediction intervals.

Analyzing hospitalization data: potential limitations of Poisson regression.

Science.gov (United States)

Weaver, Colin G; Ravani, Pietro; Oliver, Matthew J; Austin, Peter C; Quinn, Robert R

2015-08-01

Poisson regression is commonly used to analyze hospitalization data when outcomes are expressed as counts (e.g. number of days in hospital). However, data often violate the assumptions on which Poisson regression is based. More appropriate extensions of this model, while available, are rarely used. We compared hospitalization data between 206 patients treated with hemodialysis (HD) and 107 treated with peritoneal dialysis (PD) using Poisson regression and compared results from standard Poisson regression with those obtained using three other approaches for modeling count data: negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression. We examined the appropriateness of each model and compared the results obtained with each approach. During a mean 1.9 years of follow-up, 183 of 313 patients (58%) were never hospitalized (indicating an excess of 'zeros'). The data also displayed overdispersion (variance greater than mean), violating another assumption of the Poisson model. Using four criteria, we determined that the NB and ZINB models performed best. According to these two models, patients treated with HD experienced similar hospitalization rates as those receiving PD {NB rate ratio (RR): 1.04 [bootstrapped 95% confidence interval (CI): 0.49-2.20]; ZINB summary RR: 1.21 (bootstrapped 95% CI 0.60-2.46)}. Poisson and ZIP models fit the data poorly and had much larger point estimates than the NB and ZINB models [Poisson RR: 1.93 (bootstrapped 95% CI 0.88-4.23); ZIP summary RR: 1.84 (bootstrapped 95% CI 0.88-3.84)]. We found substantially different results when modeling hospitalization data, depending on the approach used. Our results argue strongly for a sound model selection process and improved reporting around statistical methods used for modeling count data. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

Science.gov (United States)

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Path integral approach to electron scattering in classical electromagnetic potential

International Nuclear Information System (INIS)

Xu Chuang; Feng Feng; Li Ying-Jun

2016-01-01

As is known to all, the electron scattering in classical electromagnetic potential is one of the most widespread applications of quantum theory. Nevertheless, many discussions about electron scattering are based upon single-particle Schrodinger equation or Dirac equation in quantum mechanics rather than the method of quantum field theory. In this paper, by using the path integral approach of quantum field theory, we perturbatively evaluate the scattering amplitude up to the second order for the electron scattering by the classical electromagnetic potential. The results we derive are convenient to apply to all sorts of potential forms. Furthermore, by means of the obtained results, we give explicit calculations for the one-dimensional electric potential. (paper)

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)

Ordinary Least Squares and Quantile Regression: An Inquiry-Based Learning Approach to a Comparison of Regression Methods

Science.gov (United States)

Helmreich, James E.; Krog, K. Peter

2018-01-01

We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex…

Explaining the heterogeneous scrapie surveillance figures across Europe: a meta-regression approach

Directory of Open Access Journals (Sweden)

Ru Giuseppe

2007-06-01

Full Text Available Abstract Background Two annual surveys, the abattoir and the fallen stock, monitor the presence of scrapie across Europe. A simple comparison between the prevalence estimates in different countries reveals that, in 2003, the abattoir survey appears to detect more scrapie in some countries. This is contrary to evidence suggesting the greater ability of the fallen stock survey to detect the disease. We applied meta-analysis techniques to study this apparent heterogeneity in the behaviour of the surveys across Europe. Furthermore, we conducted a meta-regression analysis to assess the effect of country-specific characteristics on the variability. We have chosen the odds ratios between the two surveys to inform the underlying relationship between them and to allow comparisons between the countries under the meta-regression framework. Baseline risks, those of the slaughtered populations across Europe, and country-specific covariates, available from the European Commission Report, were inputted in the model to explain the heterogeneity. Results Our results show the presence of significant heterogeneity in the odds ratios between countries and no reduction in the variability after adjustment for the different risks in the baseline populations. Three countries contributed the most to the overall heterogeneity: Germany, Ireland and The Netherlands. The inclusion of country-specific covariates did not, in general, reduce the variability except for one variable: the proportion of the total adult sheep population sampled as fallen stock by each country. A large residual heterogeneity remained in the model indicating the presence of substantial effect variability between countries. Conclusion The meta-analysis approach was useful to assess the level of heterogeneity in the implementation of the surveys and to explore the reasons for the variation between countries.

The principles of electronic and electromechanic power conversion a systems approach

CERN Document Server

Ferreira, Braham

2013-01-01

Teaching the principles of power electronics and electromechanical power conversion through a unique top down systems approach, The Principles of Electromechanical Power Conversion takes the role and system context of power conversion functions as the starting point. Following this approach, the text defines the building blocks of the system and describes the theory of how they exchange power with each other. The authors introduce a modern, simple approach to machines, which makes the principles of field oriented control and space vector theory approachable to undergraduate students as well as

Visualized attribute analysis approach for characterization and quantification of rice taste flavor using electronic tongue

Energy Technology Data Exchange (ETDEWEB)

Lu, Lin; Hu, Xianqiao [Rice Product Quality Supervision and Inspection Center, Ministry of Agriculture, China National Rice Research Institute, Hangzhou 310006 (China); Tian, Shiyi; Deng, Shaoping [College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310035 (China); Zhu, Zhiwei, E-mail: 615834652@qq.com [Rice Product Quality Supervision and Inspection Center, Ministry of Agriculture, China National Rice Research Institute, Hangzhou 310006 (China)

2016-05-05

This paper deals with a novel visualized attributive analysis approach for characterization and quantification of rice taste flavor attributes (softness, stickiness, sweetness and aroma) employing a multifrequency large-amplitude pulse voltammetric electronic tongue. Data preprocessing methods including Principal Component Analysis (PCA) and Fast Fourier Transform (FFT) were provided. An attribute characterization graph was represented for visualization of the interactive response in which each attribute responded by specific electrodes and frequencies. The model was trained using signal data from electronic tongue and attribute scores from artificial evaluation. The correlation coefficients for all attributes were over 0.9, resulting in good predictive ability of attributive analysis model preprocessed by FFT. This approach extracted more effective information about linear relationship between electronic tongue and taste flavor attribute. Results indicated that this approach can accurately quantify taste flavor attributes, and can be an efficient tool for data processing in a voltammetric electronic tongue system. - Graphical abstract: Schematic process for visualized attributive analysis approach using multifrequency large-amplitude pulse voltammetric electronic tongue for determination of rice taste flavor attribute. (a) sample; (b) sensors in electronic tongue; (c) excitation voltage program and response current signal from MLAPS; (d) similarity data matrix by data preprocessing and similarity extraction; (e) feature data matrix of attribute; (f) attribute characterization graph; (g) attribute scores predicted by the model. - Highlights: • Multifrequency large-amplitude pulse voltammetric electronic tongue was used. • A visualized attributive analysis approach was created as an efficient tool for data processing. • Rice taste flavor attribute was determined and predicted. • The attribute characterization graph was represented for visualization of the

Multiple scattering approach to the vibrational excitation of molecules by slow electrons

International Nuclear Information System (INIS)

Drukarev, G.

1976-01-01

Another approach to the problem of vibrational excitation of homonuclear two-atomic molecules by slow electrons possibly accompanied by rotational transitions is presented based on the picture of multiple scattering of an electron inside the molecule. The scattering of two fixed centers in the zero range potential model is considered. The results indicate that the multiple scattering determines the order of magnitude of the vibrational excitation cross sections in the energy region under consideration even if the zero range potential model is used. Also the connection between the multiple scattering approach and quasi-stationary molecular ion picture is established. 9 refs

Comparison of exact, efron and breslow parameter approach method on hazard ratio and stratified cox regression model

Science.gov (United States)

Fatekurohman, Mohamat; Nurmala, Nita; Anggraeni, Dian

2018-04-01

Lungs are the most important organ, in the case of respiratory system. Problems related to disorder of the lungs are various, i.e. pneumonia, emphysema, tuberculosis and lung cancer. Comparing all those problems, lung cancer is the most harmful. Considering about that, the aim of this research applies survival analysis and factors affecting the endurance of the lung cancer patient using comparison of exact, Efron and Breslow parameter approach method on hazard ratio and stratified cox regression model. The data applied are based on the medical records of lung cancer patients in Jember Paru-paru hospital on 2016, east java, Indonesia. The factors affecting the endurance of the lung cancer patients can be classified into several criteria, i.e. sex, age, hemoglobin, leukocytes, erythrocytes, sedimentation rate of blood, therapy status, general condition, body weight. The result shows that exact method of stratified cox regression model is better than other. On the other hand, the endurance of the patients is affected by their age and the general conditions.

Quantum Geometry: Relativistic energy approach to cooperative electron-nucleary-transition spectrum

Directory of Open Access Journals (Sweden)

Ольга Юрьевна Хецелиус

2014-11-01

Full Text Available An advanced relativistic energy approach is presented and applied to calculating parameters of electron-nuclear 7-transition spectra of nucleus in the atom. The intensities of the spectral satellites are defined in the relativistic version of the energy approach (S-matrix formalism, and gauge-invariant quantum-electrodynamical perturbation theory with the Dirac-Kohn-Sham density-functional zeroth approximation.

Materials and processing approaches for foundry-compatible transient electronics

Science.gov (United States)

Chang, Jan-Kai; Fang, Hui; Bower, Christopher A.; Song, Enming; Yu, Xinge; Rogers, John A.

2017-07-01

Foundry-based routes to transient silicon electronic devices have the potential to serve as the manufacturing basis for “green” electronic devices, biodegradable implants, hardware secure data storage systems, and unrecoverable remote devices. This article introduces materials and processing approaches that enable state-of-the-art silicon complementary metal-oxide-semiconductor (CMOS) foundries to be leveraged for high-performance, water-soluble forms of electronics. The key elements are (i) collections of biodegradable electronic materials (e.g., silicon, tungsten, silicon nitride, silicon dioxide) and device architectures that are compatible with manufacturing procedures currently used in the integrated circuit industry, (ii) release schemes and transfer printing methods for integration of multiple ultrathin components formed in this way onto biodegradable polymer substrates, and (iii) planarization and metallization techniques to yield interconnected and fully functional systems. Various CMOS devices and circuit elements created in this fashion and detailed measurements of their electrical characteristics highlight the capabilities. Accelerated dissolution studies in aqueous environments reveal the chemical kinetics associated with the underlying transient behaviors. The results demonstrate the technical feasibility for using foundry-based routes to sophisticated forms of transient electronic devices, with functional capabilities and cost structures that could support diverse applications in the biomedical, military, industrial, and consumer industries.

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

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.

The Public-Private Sector Wage Gap in Zambia in the 1990s: A Quantile Regression Approach

DEFF Research Database (Denmark)

Nielsen, Helena Skyt; Rosholm, Michael

2001-01-01

of economic transition, because items as privatization and deregulation were on the political agenda. The focus is placed on the public-private sector wage gap, and the results show that this gap was relatively favorable for the low-skilled and less favorable for the high-skilled. This picture was further......We investigate the determinants of wages in Zambia and based on the quantile regression approach, we analyze how their effects differ at different points in the wage distribution and over time. We use three cross-sections of Zambian household data from the early nineties, which was a period...

Testing hypotheses for differences between linear regression lines

Science.gov (United States)

Stanley J. Zarnoch

2009-01-01

Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

Fabrication Approaches to Interconnect Based Devices for Stretchable Electronics: A Review.

Science.gov (United States)

Nagels, Steven; Deferme, Wim

2018-03-03

Stretchable electronics promise to naturalize the way that we are surrounded by and interact with our devices. Sensors that can stretch and bend furthermore have become increasingly relevant as the technology behind them matures rapidly from lab-based workflows to industrially applicable production principles. Regardless of the specific materials used, creating stretchable conductors involves either the implementation of strain reliefs through insightful geometric patterning, the dispersion of stiff conductive filler in an elastomeric matrix, or the employment of intrinsically stretchable conductive materials. These basic principles however have spawned a myriad of materials systems wherein future application engineers need to find their way. This paper reports a literature study on the spectrum of different approaches towards stretchable electronics, discusses standardization of characteristic tests together with their reports and estimates matureness for industry. Patterned copper foils that are embedded in elastomeric sheets, which are closest to conventional electronic circuits processing, make up one end of the spectrum. Furthest from industry are the more recent circuits based on intrinsically stretchable liquid metals. These show extremely promising results, however, as a technology, liquid metal is not mature enough to be adapted. Printing makes up the transition between both ends, and is also well established on an industrial level, but traditionally not linked to creating electronics. Even though a certain level of maturity was found amongst the approaches that are reviewed herein, industrial adaptation for consumer electronics remains unpredictable without a designated break-through commercial application.

Fabrication Approaches to Interconnect Based Devices for Stretchable Electronics: A Review

Directory of Open Access Journals (Sweden)

Steven Nagels

2018-03-01

Full Text Available Stretchable electronics promise to naturalize the way that we are surrounded by and interact with our devices. Sensors that can stretch and bend furthermore have become increasingly relevant as the technology behind them matures rapidly from lab-based workflows to industrially applicable production principles. Regardless of the specific materials used, creating stretchable conductors involves either the implementation of strain reliefs through insightful geometric patterning, the dispersion of stiff conductive filler in an elastomeric matrix, or the employment of intrinsically stretchable conductive materials. These basic principles however have spawned a myriad of materials systems wherein future application engineers need to find their way. This paper reports a literature study on the spectrum of different approaches towards stretchable electronics, discusses standardization of characteristic tests together with their reports and estimates matureness for industry. Patterned copper foils that are embedded in elastomeric sheets, which are closest to conventional electronic circuits processing, make up one end of the spectrum. Furthest from industry are the more recent circuits based on intrinsically stretchable liquid metals. These show extremely promising results, however, as a technology, liquid metal is not mature enough to be adapted. Printing makes up the transition between both ends, and is also well established on an industrial level, but traditionally not linked to creating electronics. Even though a certain level of maturity was found amongst the approaches that are reviewed herein, industrial adaptation for consumer electronics remains unpredictable without a designated break-through commercial application.

A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis

International Nuclear Information System (INIS)

Kumar, Akansha; Tsvetkov, Pavel V.

2015-01-01

desired power peaking limits, desired effective and infinite neutron multiplication factors, high fast fission factor, high thermal efficiency in the conversion from thermal energy to electrical energy using the Brayton cycle, and high fuel burn-up. It is to be noted that we have kept the total mass of the fuel as constant. In this work, we present a module based (modular) approach to perform the optimization wherein, we have defined the following modules: single fuel pin cell, whole core, thermal–hydraulics, and energy conversion. In each of the modules we have defined a specific set of parameters and optimization objectives. The GA system (GAS), and RS together, play the role of optimizing each of the individual modules, and integrating the modules to determine the final nuclear reactor core. However, implementation of GA could lead to a local minimum or a non-unique set of parameters, those meet the specific optimization objectives. The GA code is built using Java, neutronic analysis using MCNP6, thermal–hydraulics calculations using Java, and regression analysis using R

Modeling Electronic Circular Dichroism within the Polarizable Embedding Approach

DEFF Research Database (Denmark)

Nørby, Morten S; Olsen, Jógvan Magnus Haugaard; Steinmann, Casper

2017-01-01

We present a systematic investigation of the key components needed to model single chromophore electronic circular dichroism (ECD) within the polarizable embedding (PE) approach. By relying on accurate forms of the embedding potential, where especially the inclusion of local field effects...... are in focus, we show that qualitative agreement between rotatory strength parameters calculated by full quantum mechanical calculations and the more efficient embedding calculations can be obtained. An important aspect in the computation of reliable absorption parameters is the need for conformational...... sampling. We show that a significant number of snapshots are needed to avoid artifacts in the calculated electronic circular dichroism parameters due to insufficient configurational sampling, thus highlighting the efficiency of the PE model....

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).

Electronic resource management systems a workflow approach

CERN Document Server

Anderson, Elsa K

2014-01-01

To get to the bottom of a successful approach to Electronic Resource Management (ERM), Anderson interviewed staff at 11 institutions about their ERM implementations. Among her conclusions, presented in this issue of Library Technology Reports, is that grasping the intricacies of your workflow-analyzing each step to reveal the gaps and problems-at the beginning is crucial to selecting and implementing an ERM. Whether the system will be used to fill a gap, aggregate critical data, or replace a tedious manual process, the best solution for your library depends on factors such as your current soft

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...

Straight line fitting and predictions: On a marginal likelihood approach to linear regression and errors-in-variables models

Science.gov (United States)

Christiansen, Bo

2015-04-01

Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.

Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi...

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

Directory of Open Access Journals (Sweden)

Zhi-Sai Ma

2017-01-01

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

The Prediction Properties of Inverse and Reverse Regression for the Simple Linear Calibration Problem

Science.gov (United States)

Parker, Peter A.; Geoffrey, Vining G.; Wilson, Sara R.; Szarka, John L., III; Johnson, Nels G.

2010-01-01

The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.

A Graphical, Self-Organizing Approach to Classifying Electronic Meeting Output.

Science.gov (United States)

Orwig, Richard E.; Chen, Hsinchun; Nunamaker, Jay F., Jr.

1997-01-01

Describes research using an artificial intelligence approach in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information…

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

Science.gov (United States)

Duda, Piotr; Jaworski, Maciej; Rutkowski, Leszek

2018-03-01

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction.

Zeroth order regular approximation approach to electric dipole moment interactions of the electron

Science.gov (United States)

Gaul, Konstantin; Berger, Robert

2017-07-01

A quasi-relativistic two-component approach for an efficient calculation of P ,T -odd interactions caused by a permanent electric dipole moment of the electron (eEDM) is presented. The approach uses a (two-component) complex generalized Hartree-Fock and a complex generalized Kohn-Sham scheme within the zeroth order regular approximation. In applications to select heavy-elemental polar diatomic molecular radicals, which are promising candidates for an eEDM experiment, the method is compared to relativistic four-component electron-correlation calculations and confirms values for the effective electric field acting on the unpaired electron for RaF, BaF, YbF, and HgF. The calculations show that purely relativistic effects, involving only the lower component of the Dirac bi-spinor, are well described by treating only the upper component explicitly.

Marginal longitudinal semiparametric regression via penalized splines

KAUST Repository

Al Kadiri, M.

2010-08-01

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.

Marginal longitudinal semiparametric regression via penalized splines

KAUST Repository

Al Kadiri, M.; Carroll, R.J.; Wand, M.P.

2010-01-01

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.

HYBRID DATA APPROACH FOR SELECTING EFFECTIVE TEST CASES DURING THE REGRESSION TESTING

OpenAIRE

Mohan, M.; Shrimali, Tarun

2017-01-01

In the software industry, software testing becomes more important in the entire software development life cycle. Software testing is one of the fundamental components of software quality assurances. Software Testing Life Cycle (STLC)is a process involved in testing the complete software, which includes Regression Testing, Unit Testing, Smoke Testing, Integration Testing, Interface Testing, System Testing & etc. In the STLC of Regression testing, test case selection is one of the most importan...

When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization

Science.gov (United States)

Shoff, Carla; Chen, Vivian Yi-Ju; Yang, Tse-Chuan

2014-01-01

Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilization in the US and found that most of the relationships between late or not prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study is to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employ an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this innovative framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and the results are changed after considering the spatially lagged effect of prenatal care utilization. The GWR-SL approach allows us to gain a place-specific understanding of prenatal care utilization in US counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., OLS and spatial lag models) and found that GWR-SL is the preferred modeling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilization across space, and determine whether and how the level of prenatal care utilization in neighboring counties matters. PMID:24893033

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

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

2011-01-01

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

Electronic excitation of atoms and molecules by electron impact in a linear algebraic, separable potential approach

International Nuclear Information System (INIS)

Collins, L.A.; Schneider, B.I.

1984-01-01

The linear algebraic, separable potential approach is applied to the electronic excitation of atoms and molecules by electron impact. By representing the exchange and off-diagonal direct terms on a basis, the standard set of coupled inelastic equations is reduced to a set of elastic inhomogeneous equations. The procedure greatly simplifies the formulation by allowing a large portion of the problem to be handled by standard bound-state techniques and by greatly reducing the order of the scattering equations that must be solved. Application is made to the excitation of atomic hydrogen in the three-state close-coupling (1s, 2s, 2p) approximation. (author)

A novel system architecture for the national integration of electronic health records: a semi-centralized approach.

Science.gov (United States)

AlJarullah, Asma; El-Masri, Samir

2013-08-01

The goal of a national electronic health records integration system is to aggregate electronic health records concerning a particular patient at different healthcare providers' systems to provide a complete medical history of the patient. It holds the promise to address the two most crucial challenges to the healthcare systems: improving healthcare quality and controlling costs. Typical approaches for the national integration of electronic health records are a centralized architecture and a distributed architecture. This paper proposes a new approach for the national integration of electronic health records, the semi-centralized approach, an intermediate solution between the centralized architecture and the distributed architecture that has the benefits of both approaches. The semi-centralized approach is provided with a clearly defined architecture. The main data elements needed by the system are defined and the main system modules that are necessary to achieve an effective and efficient functionality of the system are designed. Best practices and essential requirements are central to the evolution of the proposed architecture. The proposed architecture will provide the basis for designing the simplest and the most effective systems to integrate electronic health records on a nation-wide basis that maintain integrity and consistency across locations, time and systems, and that meet the challenges of interoperability, security, privacy, maintainability, mobility, availability, scalability, and load balancing.

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.

Flexible competing risks regression modeling and goodness-of-fit

DEFF Research Database (Denmark)

Scheike, Thomas; Zhang, Mei-Jie

2008-01-01

In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....

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.

Predictability of extreme weather events for NE U.S.: improvement of the numerical prediction using a Bayesian regression approach

Science.gov (United States)

Yang, J.; Astitha, M.; Anagnostou, E. N.; Hartman, B.; Kallos, G. B.

2015-12-01

Weather prediction accuracy has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment. Concurrently, weather forecast tools and techniques have evolved with improved forecast skill as numerical prediction techniques are strengthened by increased super-computing resources. In this study, we examine the combination of two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) by utilizing a Bayesian regression approach to improve the prediction of extreme weather events for NE U.S. The basic concept behind the Bayesian regression approach is to take advantage of the strengths of two atmospheric modeling systems and, similar to the multi-model ensemble approach, limit their weaknesses which are related to systematic and random errors in the numerical prediction of physical processes. The first part of this study is focused on retrospective simulations of seventeen storms that affected the region in the period 2004-2013. Optimal variances are estimated by minimizing the root mean square error and are applied to out-of-sample weather events. The applicability and usefulness of this approach are demonstrated by conducting an error analysis based on in-situ observations from meteorological stations of the National Weather Service (NWS) for wind speed and wind direction, and NCEP Stage IV radar data, mosaicked from the regional multi-sensor for precipitation. The preliminary results indicate a significant improvement in the statistical metrics of the modeled-observed pairs for meteorological variables using various combinations of the sixteen events as predictors of the seventeenth. This presentation will illustrate the implemented methodology and the obtained results for wind speed, wind direction and precipitation, as well as set the research steps that will be

Approximating prediction uncertainty for random forest regression models

Science.gov (United States)

John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach

International Nuclear Information System (INIS)

Ruan, D; Yang, Y; Cao, M; Hu, P; Low, D

2014-01-01

Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme

SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach

Energy Technology Data Exchange (ETDEWEB)

Ruan, D; Yang, Y; Cao, M; Hu, P; Low, D [UCLA, Los Angeles, CA (United States)

2014-06-01

Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme

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%.

Modeling oil production based on symbolic regression

International Nuclear Information System (INIS)

Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju

2015-01-01

Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak

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.

Coupled forward-backward trajectory approach for nonequilibrium electron-ion dynamics

Science.gov (United States)

Sato, Shunsuke A.; Kelly, Aaron; Rubio, Angel

2018-04-01

We introduce a simple ansatz for the wave function of a many-body system based on coupled forward and backward propagating semiclassical trajectories. This method is primarily aimed at, but not limited to, treating nonequilibrium dynamics in electron-phonon systems. The time evolution of the system is obtained from the Euler-Lagrange variational principle, and we show that this ansatz yields Ehrenfest mean-field theory in the limit that the forward and backward trajectories are orthogonal, and in the limit that they coalesce. We investigate accuracy and performance of this method by simulating electronic relaxation in the spin-boson model and the Holstein model. Although this method involves only pairs of semiclassical trajectories, it shows a substantial improvement over mean-field theory, capturing quantum coherence of nuclear dynamics as well as electron-nuclear correlations. This improvement is particularly evident in nonadiabatic systems, where the accuracy of this coupled trajectory method extends well beyond the perturbative electron-phonon coupling regime. This approach thus provides an attractive route forward to the ab initio description of relaxation processes, such as thermalization, in condensed phase systems.

Approximate median regression for complex survey data with skewed response.

Science.gov (United States)

Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti; Fitzmaurice, Garrett M; Pan, Yi

2016-12-01

The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling, and weighting. In this article, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a double-transform-both-sides (DTBS)'based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same Box-Cox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudo-likelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey. © 2016, The International Biometric Society.

New Statistical Multiparticle Approach to the Acceleration of Electrons by the Ion Field in Plasmas

Directory of Open Access Journals (Sweden)

Eugene Oks

2010-01-01

Full Text Available The phenomenon of the acceleration of the (perturbing electrons by the ion field (AEIF significantly reduces Stark widths and shifts in plasmas of relatively high densities and/or relatively low temperature. Our previous analytical calculations of the AEIF were based on the dynamical treatment: the starting point was the ion-microfield-caused changes of the trajectories and velocities of individual perturbing electrons. In the current paper, we employ a statistical approach: the starting point is the electron velocity distribution function modified by the ion microfield. The latter had been calculated by Romanovsky and Ebeling in the multiparticle description of the ion microfield. The result shows again the reduction of the electron Stark broadening. Thus two totally different analytical approaches (dynamical and statistical agree with each other and therefore disprove the corresponding recent fully-numerical simulations by Stambulchik et al. that claimed an increase of the electron Stark broadening.

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/.

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%.

Electronic structure of FeTiSb using relativistic and scalar-relativistic approaches

Energy Technology Data Exchange (ETDEWEB)

Sahariya, Jagrati [Department of Physics, Manipal University Jaipur, Jaipur-303007, Rajasthan (India); Mund, H. S., E-mail: hmoond@gmail.com [Department of Physics, M. L. Sukhadia University, Udaipur-313001, Rajasthan (India)

2016-05-06

Electronic and magnetic properties of FeTiSb have been reported. The calculations are performed using spin polarized relativistic Korringa-Kohn-Rostoker scheme based on Green’s function method. Within SPR-KKR a fully relativistic and scalar-relativistic approaches have been used to investigate electronic structure of FeTiSb. Energy bands, total and partial density of states, atom specific magnetic moment along with total moment of FeTiSb alloys are presented.

Finite Algorithms for Robust Linear Regression

DEFF Research Database (Denmark)

Madsen, Kaj; Nielsen, Hans Bruun

1990-01-01

The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...

Continuum multiple-scattering approach to electron-molecule scattering and molecular photoionization

International Nuclear Information System (INIS)

Dehmer, J.L.; Dill, D.

1979-01-01

The multiple-scattering approach to the electronic continuum of molecules is described. The continuum multiple-scattering model (CMSM) was developed as a survey tool and, as such was required to satisfy two requirements. First, it had to have a very broad scope, which means (i) molecules of arbitrary geometry and complexity containing any atom in the periodic system, (ii) continuum electron energies from 0-1000 eV, and (iii) capability to treat a large range of processes involving both photoionization and electron scattering. Second, the structure of the theory was required to lend itself to transparent, physical interpretation of major spectral features such as shape resonances. A comprehensive theoretical framework for the continuum multiple scattering method is presented, as well as its applications to electron-molecule scattering and molecular photoionization. Highlights of recent applications in these two areas are reviewed. The major impact of the resulting studies over the last few years has been to establish the importance of shape resonances in electron collisions and photoionization of practically all (non-hydride) molecules

Observation of superconducting fluxons by transmission electron microscopy: A Fourier space approach to calculate the electron optical phase shifts and images

International Nuclear Information System (INIS)

Beleggia, M.; Pozzi, G.

2001-01-01

An approach is presented for the calculation of the electron optical phase shift experienced by high-energy electrons in a transmission electron microscope, when they interact with the magnetic field associated with superconducting fluxons in a thin specimen tilted with respect to the beam. It is shown that by decomposing the vector potential in its Fourier components and by calculating the phase shift of each component separately, it is possible to obtain the Fourier transform of the electron optical phase shift, which can be inverted either analytically or numerically. It will be shown how this method can be used to recover the result, previously obtained by the real-space approach, relative to the case of a straight flux tube perpendicular to the specimen surfaces. Then the method is applied to the case of a London fluxon in a thin film, where the bending and the broadening of the magnetic-field lines due to the finite specimen thickness are now correctly taken into account and not treated approximately by means of a parabolic fit. Finally, it will be shown how simple models for the pancake structure of the fluxon can be analyzed within this framework and the main features of electron transmission images predicted

Novel approaches to study low-energy electron-induced damage to DNA oligonucleotides

International Nuclear Information System (INIS)

Rackwitz, Jenny; Bald, Ilko; Ranković, Miloš Lj; Milosavljević, Aleksandar R

2015-01-01

The novel approach of DNA origami structures as templates for precise quantification of various well- defined oligonucleotides provides the opportunity to determine the sensitivity of complex DNA sequences towards low-energy electrons. (paper)

Detection of Outliers in Regression Model for Medical Data

Directory of Open Access Journals (Sweden)

Stephen Raj S

2017-07-01

Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.

Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach

Science.gov (United States)

Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo

2009-01-01

We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...

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.

An alternative approach to the determination of scaling law expressions for the L–H transition in Tokamaks utilizing classification tools instead of regression

International Nuclear Information System (INIS)

Gaudio, P; Gelfusa, M; Lupelli, I; Murari, A; Vega, J

2014-01-01

A new approach to determine the power law expressions for the threshold between the H and L mode of confinement is presented. The method is based on two powerful machine learning tools for classification: neural networks and support vector machines. Using as inputs clear examples of the systems on either side of the transition, the machine learning tools learn the input–output mapping corresponding to the equations of the boundary separating the confinement regimes. Systematic tests with synthetic data show that the machine learning tools provide results competitive with traditional statistical regression and more robust against random noise and systematic errors. The developed tools have then been applied to the multi-machine International Tokamak Physics Activity International Global Threshold Database of validated ITER-like Tokamak discharges. The machine learning tools converge on the same scaling law parameters obtained with non-linear regression. On the other hand, the developed tools allow a reduction of 50% of the uncertainty in the extrapolations to ITER. Therefore the proposed approach can effectively complement traditional regression since its application poses much less stringent requirements on the experimental data, to be used to determine the scaling laws, because they do not require examples exactly at the moment of the transition. (paper)

Testing the equality of nonparametric regression curves based on ...

African Journals Online (AJOL)

Abstract. In this work we propose a new methodology for the comparison of two regression functions f1 and f2 in the case of homoscedastic error structure and a fixed design. Our approach is based on the empirical Fourier coefficients of the regression functions f1 and f2 respectively. As our main results we obtain the ...

Quantile Regression With Measurement Error

KAUST Repository

Wei, Ying

2009-08-27

Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.

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.

Alternative regression models to assess increase in childhood BMI.

Science.gov (United States)

Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

2008-09-08

Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

A novel approach to electron data background treatment in an online wide-angle spectrometer for laser-accelerated ion and electron bunches

Science.gov (United States)

Lindner, F. H.; Bin, J. H.; Englbrecht, F.; Haffa, D.; Bolton, P. R.; Gao, Y.; Hartmann, J.; Hilz, P.; Kreuzer, C.; Ostermayr, T. M.; Rösch, T. F.; Speicher, M.; Parodi, K.; Thirolf, P. G.; Schreiber, J.

2018-01-01

Laser-based ion acceleration is driven by electrical fields emerging when target electrons absorb laser energy and consecutively leave the target material. A direct correlation between these electrons and the accelerated ions is thus to be expected and predicted by theoretical models. We report on a modified wide-angle spectrometer, allowing the simultaneous characterization of angularly resolved energy distributions of both ions and electrons. Equipped with online pixel detectors, the RadEye1 detectors, the investigation of this correlation gets attainable on a single shot basis. In addition to first insights, we present a novel approach for reliably extracting the primary electron energy distribution from the interfering secondary radiation background. This proves vitally important for quantitative extraction of average electron energies (temperatures) and emitted total charge.

Theoretical study of molecular vibrations in electron momentum spectroscopy experiments on furan: An analytical versus a molecular dynamical approach

International Nuclear Information System (INIS)

Morini, Filippo; Deleuze, Michael S.; Watanabe, Noboru; Takahashi, Masahiko

2015-01-01

The influence of thermally induced nuclear dynamics (molecular vibrations) in the initial electronic ground state on the valence orbital momentum profiles of furan has been theoretically investigated using two different approaches. The first of these approaches employs the principles of Born-Oppenheimer molecular dynamics, whereas the so-called harmonic analytical quantum mechanical approach resorts to an analytical decomposition of contributions arising from quantized harmonic vibrational eigenstates. In spite of their intrinsic differences, the two approaches enable consistent insights into the electron momentum distributions inferred from new measurements employing electron momentum spectroscopy and an electron impact energy of 1.2 keV. Both approaches point out in particular an appreciable influence of a few specific molecular vibrations of A 1 symmetry on the 9a 1 momentum profile, which can be unravelled from considerations on the symmetry characteristics of orbitals and their energy spacing

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

Science.gov (United States)

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

Development and application of a 2-electron reduced density matrix approach to electron transport via molecular junctions

Science.gov (United States)

Hoy, Erik P.; Mazziotti, David A.; Seideman, Tamar

2017-11-01

Can an electronic device be constructed using only a single molecule? Since this question was first asked by Aviram and Ratner in the 1970s [Chem. Phys. Lett. 29, 277 (1974)], the field of molecular electronics has exploded with significant experimental advancements in the understanding of the charge transport properties of single molecule devices. Efforts to explain the results of these experiments and identify promising new candidate molecules for molecular devices have led to the development of numerous new theoretical methods including the current standard theoretical approach for studying single molecule charge transport, i.e., the non-equilibrium Green's function formalism (NEGF). By pairing this formalism with density functional theory (DFT), a wide variety of transport problems in molecular junctions have been successfully treated. For some systems though, the conductance and current-voltage curves predicted by common DFT functionals can be several orders of magnitude above experimental results. In addition, since density functional theory relies on approximations to the exact exchange-correlation functional, the predicted transport properties can show significant variation depending on the functional chosen. As a first step to addressing this issue, the authors have replaced density functional theory in the NEGF formalism with a 2-electron reduced density matrix (2-RDM) method, creating a new approach known as the NEGF-RDM method. 2-RDM methods provide a more accurate description of electron correlation compared to density functional theory, and they have lower computational scaling compared to wavefunction based methods of similar accuracy. Additionally, 2-RDM methods are capable of capturing static electron correlation which is untreatable by existing NEGF-DFT methods. When studying dithiol alkane chains and dithiol benzene in model junctions, the authors found that the NEGF-RDM predicts conductances and currents that are 1-2 orders of magnitude below

Zero-Shot Learning via Attribute Regression and Class Prototype Rectification.

Science.gov (United States)

Luo, Changzhi; Li, Zhetao; Huang, Kaizhu; Feng, Jiashi; Wang, Meng

2018-02-01

Zero-shot learning (ZSL) aims at classifying examples for unseen classes (with no training examples) given some other seen classes (with training examples). Most existing approaches exploit intermedia-level information (e.g., attributes) to transfer knowledge from seen classes to unseen classes. A common practice is to first learn projections from samples to attributes on seen classes via a regression method, and then apply such projections to unseen classes directly. However, it turns out that such a manner of learning strategy easily causes projection domain shift problem and hubness problem, which hinder the performance of ZSL task. In this paper, we also formulate ZSL as an attribute regression problem. However, different from general regression-based solutions, the proposed approach is novel in three aspects. First, a class prototype rectification method is proposed to connect the unseen classes to the seen classes. Here, a class prototype refers to a vector representation of a class, and it is also known as a class center, class signature, or class exemplar. Second, an alternating learning scheme is proposed for jointly performing attribute regression and rectifying the class prototypes. Finally, a new objective function which takes into consideration both the attribute regression accuracy and the class prototype discrimination is proposed. By introducing such a solution, domain shift problem and hubness problem can be mitigated. Experimental results on three public datasets (i.e., CUB200-2011, SUN Attribute, and aPaY) well demonstrate the effectiveness of our approach.

Time series regression model for infectious disease and weather.

Science.gov (United States)

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Interacting electrons theory and computational approaches

CERN Document Server

Martin, Richard M; Ceperley, David M

2016-01-01

Recent progress in the theory and computation of electronic structure is bringing an unprecedented level of capability for research. Many-body methods are becoming essential tools vital for quantitative calculations and understanding materials phenomena in physics, chemistry, materials science and other fields. This book provides a unified exposition of the most-used tools: many-body perturbation theory, dynamical mean field theory and quantum Monte Carlo simulations. Each topic is introduced with a less technical overview for a broad readership, followed by in-depth descriptions and mathematical formulation. Practical guidelines, illustrations and exercises are chosen to enable readers to appreciate the complementary approaches, their relationships, and the advantages and disadvantages of each method. This book is designed for graduate students and researchers who want to use and understand these advanced computational tools, get a broad overview, and acquire a basis for participating in new developments.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

Science.gov (United States)

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Alternative regression models to assess increase in childhood BMI

Directory of Open Access Journals (Sweden)

Mansmann Ulrich

2008-09-01

Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

An effective approach for choosing an electronic health record.

Science.gov (United States)

Rowley, Robert

2009-01-01

With government stimulus money becoming available to encourage healthcare facilities to adopt electronic health record (EHR) systems, the decision to move forward with implementing an EHR system has taken on an urgency not previously seen. The EHR landscape is evolving rapidly and the underlying technology platform is becoming increasingly interconnected. One must make sure that an EHR decision does not lock oneself into technology obsolescence. The best approach for evaluating an EHR is on the basis of:usability, interoperability, and affordability.

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.…

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

Science.gov (United States)

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

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.

A multiple regression method for genomewide association studies ...

Indian Academy of Sciences (India)

Bujun Mei

2018-06-07

Jun 7, 2018 ... Similar to the typical genomewide association tests using LD ... new approach performed validly when the multiple regression based on linkage method was employed. .... the model, two groups of scenarios were simulated.

A novel approach for honey pollen profile assessment using an electronic tongue and chemometric tools

Energy Technology Data Exchange (ETDEWEB)

Dias, Luís G., E-mail: ldias@ipb.pt [Escola Superior Agrária, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5301-855 Bragança (Portugal); CQ-VR, Centro de Química – Vila Real, University of Trás-os-Montes e Alto Douro, Apartado 1013, 5001-801 Vila Real (Portugal); Veloso, Ana C.A. [Instituto Politécnico de Coimbra, ISEC, DEQB, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra (Portugal); CEB-Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga (Portugal); Sousa, Mara E.B.C.; Estevinho, Letícia [CIMO-Escola Superior Agrária, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5301-855 Bragança (Portugal); Machado, Adélio A.S.C. [LAQUIPAI – Laboratório de Química Inorgânica Pura e de Aplicação Interdisciplinar, Departamento de Química, Faculdade de Ciências da, Universidade do Porto, Rua Campo Alegre n°. 687, 4169-007 Porto (Portugal); and others

2015-11-05

Nowadays the main honey producing countries require accurate labeling of honey before commercialization, including floral classification. Traditionally, this classification is made by melissopalynology analysis, an accurate but time-consuming task requiring laborious sample pre-treatment and high-skilled technicians. In this work the potential use of a potentiometric electronic tongue for pollinic assessment is evaluated, using monofloral and polyfloral honeys. The results showed that after splitting honeys according to color (white, amber and dark), the novel methodology enabled quantifying the relative percentage of the main pollens (Castanea sp., Echium sp., Erica sp., Eucaliptus sp., Lavandula sp., Prunus sp., Rubus sp. and Trifolium sp.). Multiple linear regression models were established for each type of pollen, based on the best sensors' sub-sets selected using the simulated annealing algorithm. To minimize the overfitting risk, a repeated K-fold cross-validation procedure was implemented, ensuring that at least 10–20% of the honeys were used for internal validation. With this approach, a minimum average determination coefficient of 0.91 ± 0.15 was obtained. Also, the proposed technique enabled the correct classification of 92% and 100% of monofloral and polyfloral honeys, respectively. The quite satisfactory performance of the novel procedure for quantifying the relative pollen frequency may envisage its applicability for honey labeling and geographical origin identification. Nevertheless, this approach is not a full alternative to the traditional melissopalynologic analysis; it may be seen as a practical complementary tool for preliminary honey floral classification, leaving only problematic cases for pollinic evaluation. - Highlights: • Honey's floral origin labeling is a legal requirement. • Melissopalynology analysis usually used to evaluate pollens profile is laborious. • A novel E-tongue based approach is applied to assess pollens

A novel approach for honey pollen profile assessment using an electronic tongue and chemometric tools

International Nuclear Information System (INIS)

Dias, Luís G.; Veloso, Ana C.A.; Sousa, Mara E.B.C.; Estevinho, Letícia; Machado, Adélio A.S.C.

2015-01-01

Nowadays the main honey producing countries require accurate labeling of honey before commercialization, including floral classification. Traditionally, this classification is made by melissopalynology analysis, an accurate but time-consuming task requiring laborious sample pre-treatment and high-skilled technicians. In this work the potential use of a potentiometric electronic tongue for pollinic assessment is evaluated, using monofloral and polyfloral honeys. The results showed that after splitting honeys according to color (white, amber and dark), the novel methodology enabled quantifying the relative percentage of the main pollens (Castanea sp., Echium sp., Erica sp., Eucaliptus sp., Lavandula sp., Prunus sp., Rubus sp. and Trifolium sp.). Multiple linear regression models were established for each type of pollen, based on the best sensors' sub-sets selected using the simulated annealing algorithm. To minimize the overfitting risk, a repeated K-fold cross-validation procedure was implemented, ensuring that at least 10–20% of the honeys were used for internal validation. With this approach, a minimum average determination coefficient of 0.91 ± 0.15 was obtained. Also, the proposed technique enabled the correct classification of 92% and 100% of monofloral and polyfloral honeys, respectively. The quite satisfactory performance of the novel procedure for quantifying the relative pollen frequency may envisage its applicability for honey labeling and geographical origin identification. Nevertheless, this approach is not a full alternative to the traditional melissopalynologic analysis; it may be seen as a practical complementary tool for preliminary honey floral classification, leaving only problematic cases for pollinic evaluation. - Highlights: • Honey's floral origin labeling is a legal requirement. • Melissopalynology analysis usually used to evaluate pollens profile is laborious. • A novel E-tongue based approach is applied to assess pollens relative

Hierarchical Matching and Regression with Application to Photometric Redshift Estimation

Science.gov (United States)

Murtagh, Fionn

2017-06-01

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

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

Science.gov (United States)

Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

2017-01-01

This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study

Science.gov (United States)

Zhong, Victor W; Obeid, Jihad S; Craig, Jean B; Pfaff, Emily R; Thomas, Joan; Jaacks, Lindsay M; Beavers, Daniel P; Carey, Timothy S; Lawrence, Jean M; Dabelea, Dana; Hamman, Richard F; Bowlby, Deborah A; Pihoker, Catherine; Saydah, Sharon H

2016-01-01

Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data. Materials and Methods Presumptive diabetes cases diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified. Results We developed a stepwise surveillance approach using billing code–based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with “other” type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. Conclusion EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed. PMID:27107449

Nuclear-electronic orbital reduced explicitly correlated Hartree-Fock approach: Restricted basis sets and open-shell systems

International Nuclear Information System (INIS)

Brorsen, Kurt R.; Sirjoosingh, Andrew; Pak, Michael V.; Hammes-Schiffer, Sharon

2015-01-01

The nuclear electronic orbital (NEO) reduced explicitly correlated Hartree-Fock (RXCHF) approach couples select electronic orbitals to the nuclear orbital via Gaussian-type geminal functions. This approach is extended to enable the use of a restricted basis set for the explicitly correlated electronic orbitals and an open-shell treatment for the other electronic orbitals. The working equations are derived and the implementation is discussed for both extensions. The RXCHF method with a restricted basis set is applied to HCN and FHF − and is shown to agree quantitatively with results from RXCHF calculations with a full basis set. The number of many-particle integrals that must be calculated for these two molecules is reduced by over an order of magnitude with essentially no loss in accuracy, and the reduction factor will increase substantially for larger systems. Typically, the computational cost of RXCHF calculations with restricted basis sets will scale in terms of the number of basis functions centered on the quantum nucleus and the covalently bonded neighbor(s). In addition, the RXCHF method with an odd number of electrons that are not explicitly correlated to the nuclear orbital is implemented using a restricted open-shell formalism for these electrons. This method is applied to HCN + , and the nuclear densities are in qualitative agreement with grid-based calculations. Future work will focus on the significance of nonadiabatic effects in molecular systems and the further enhancement of the NEO-RXCHF approach to accurately describe such effects

Nuclear-electronic orbital reduced explicitly correlated Hartree-Fock approach: Restricted basis sets and open-shell systems

Energy Technology Data Exchange (ETDEWEB)

Brorsen, Kurt R.; Sirjoosingh, Andrew; Pak, Michael V.; Hammes-Schiffer, Sharon, E-mail: shs3@illinois.edu [Department of Chemistry, University of Illinois at Urbana-Champaign, 600 South Mathews Ave., Urbana, Illinois 61801 (United States)

2015-06-07

The nuclear electronic orbital (NEO) reduced explicitly correlated Hartree-Fock (RXCHF) approach couples select electronic orbitals to the nuclear orbital via Gaussian-type geminal functions. This approach is extended to enable the use of a restricted basis set for the explicitly correlated electronic orbitals and an open-shell treatment for the other electronic orbitals. The working equations are derived and the implementation is discussed for both extensions. The RXCHF method with a restricted basis set is applied to HCN and FHF{sup −} and is shown to agree quantitatively with results from RXCHF calculations with a full basis set. The number of many-particle integrals that must be calculated for these two molecules is reduced by over an order of magnitude with essentially no loss in accuracy, and the reduction factor will increase substantially for larger systems. Typically, the computational cost of RXCHF calculations with restricted basis sets will scale in terms of the number of basis functions centered on the quantum nucleus and the covalently bonded neighbor(s). In addition, the RXCHF method with an odd number of electrons that are not explicitly correlated to the nuclear orbital is implemented using a restricted open-shell formalism for these electrons. This method is applied to HCN{sup +}, and the nuclear densities are in qualitative agreement with grid-based calculations. Future work will focus on the significance of nonadiabatic effects in molecular systems and the further enhancement of the NEO-RXCHF approach to accurately describe such effects.

A modified linear algebraic approach to electron scattering using cubic splines

International Nuclear Information System (INIS)

Kinney, R.A.

1986-01-01

A modified linear algebraic approach to the solution of the Schrodiner equation for low-energy electron scattering is presented. The method uses a piecewise cubic-spline approximation of the wavefunction. Results in the static-potential and the static-exchange approximations for e - +H s-wave scattering are compared with unmodified linear algebraic and variational linear algebraic methods. (author)

Age Regression in the Treatment of Anger in a Prison Setting.

Science.gov (United States)

Eisel, Harry E.

1988-01-01

Incorporated hypnotherapy with age regression into cognitive therapeutic approach with prisoners having history of anger. Technique involved age regression to establish first significant event causing current anger, catharsis of feelings for original event, and reorientation of event while under hypnosis. Results indicated decrease in acting-out…

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

Science.gov (United States)

Hu, Yi-Chung

2014-01-01

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

Communication: Electronic and transport properties of molecular junctions under a finite bias: A dual mean field approach

International Nuclear Information System (INIS)

Liu, Shuanglong; Feng, Yuan Ping; Zhang, Chun

2013-01-01

We show that when a molecular junction is under an external bias, its properties cannot be uniquely determined by the total electron density in the same manner as the density functional theory for ground state properties. In order to correctly incorporate bias-induced nonequilibrium effects, we present a dual mean field (DMF) approach. The key idea is that the total electron density together with the density of current-carrying electrons are sufficient to determine the properties of the system. Two mean fields, one for current-carrying electrons and the other one for equilibrium electrons can then be derived. Calculations for a graphene nanoribbon junction show that compared with the commonly used ab initio transport theory, the DMF approach could significantly reduce the electric current at low biases due to the non-equilibrium corrections to the mean field potential in the scattering region

New Approach For Detection Of Irradiated Spices Using Electron Spin Resonance (ESR)

International Nuclear Information System (INIS)

FARAG, S.A.; SHAMS EL DIEEN, N.M.M.

2010-01-01

Black pepper and anise samples were irradiated with different doses of gamma rays (5, 10 and 20 kGy) then the irradiated samples were stored at room temperature (20 0 C, 70-75 % RH) for one year. The measurements of free radicals were carried out by electron spin resonance (ESR) at different intervals (3, 6, 9 and 12 months). A series of signals tentatively described as cellulose-like and complex radical observed at G values were 2.01027 for black pepper and 2.01019 for anise. The ESR signals of irradiated spices showed a directly proportional relationship for increasing dose with increasing intensity of signal. A relationship was noticed as polynomial regression analysis resulted between signals of ESR intensity and applied doses with significant values of correlation coefficient (R 2 ). All combination treatments of thermal and irradiation beside long storage caused significant reduction of ESR intensity of irradiated black pepper and anise. Upon using low doses as 1, 2 and 3 kGy for re-irradiation, the irradiated samples (10 and 20 kGy) increased the power of ESR intensity. The enhancement effect was markedly increased. For example, the irradiated black pepper (10 kGy) increased the ESR intensity with high percentages as 49.19%, 69.23% and 89.68% while the high dose (20 kGy) caused increase by 39.96%, 69.05% and 96.90% for irradiated black pepper samples. This approach with that technique can be used easily to overcome the main disadvantages of ESR signals fading especially at the end of storage period.

Electron microscopy approach for the visualization of the epithelial and endothelial glycocalyx.

Science.gov (United States)

Chevalier, L; Selim, J; Genty, D; Baste, J M; Piton, N; Boukhalfa, I; Hamzaoui, M; Pareige, P; Richard, V

2017-06-01

This study presents a methodological approach for the visualization of the glycocalyx by electron microscopy. The glycocalyx is a three dimensional network mainly composed of glycolipids, glycoproteins and proteoglycans associated with the plasma membrane. Since less than a decade, the epithelial and endothelial glycocalyx proved to play an important role in physiology and pathology, increasing its research interest especially in vascular functions. Therefore, visualization of the glycocalyx requires reliable techniques and its preservation remains challenging due to its fragile and dynamic organization, which is highly sensitive to the different process steps for electron microscopy sampling. In this study, chemical fixation was performed by perfusion as a good alternative to conventional fixation. Additional lanthanum nitrate in the fixative enhances staining of the glycocalyx in transmission electron microscopy bright field and improves its visualization by detecting the elastic scattered electrons, thus providing a chemical contrast. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Quantitative vs. qualitative approaches to the electronic structure of solids

International Nuclear Information System (INIS)

Oliva, J.M.; Llunell, Miquel; Alemany, Pere; Canadell, Enric

2003-01-01

The usefulness of qualitative and quantitative theoretical approaches in solid state chemistry is discussed by considering three different types of problems: (a) the distribution of boron and carbon atoms in MB 2 C 2 (M=Ca, La, etc.) phases, (b) the band structure and Fermi surface of low-dimensional transition metal oxides and bronzes, and (c) the correlation between the crystal and electronic structure of the ternary nitride Ca 2 AuN

Analysis of sparse data in logistic regression in medical research: A newer approach

Directory of Open Access Journals (Sweden)

S Devika

2016-01-01

Full Text Available Background and Objective: In the analysis of dichotomous type response variable, logistic regression is usually used. However, the performance of logistic regression in the presence of sparse data is questionable. In such a situation, a common problem is the presence of high odds ratios (ORs with very wide 95% confidence interval (CI (OR: >999.999, 95% CI: 999.999. In this paper, we addressed this issue by using penalized logistic regression (PLR method. Materials and Methods: Data from case-control study on hyponatremia and hiccups conducted in Christian Medical College, Vellore, Tamil Nadu, India was used. The outcome variable was the presence/absence of hiccups and the main exposure variable was the status of hyponatremia. Simulation dataset was created with different sample sizes and with a different number of covariates. Results: A total of 23 cases and 50 controls were used for the analysis of ordinary and PLR methods. The main exposure variable hyponatremia was present in nine (39.13% of the cases and in four (8.0% of the controls. Of the 23 hiccup cases, all were males and among the controls, 46 (92.0% were males. Thus, the complete separation between gender and the disease group led into an infinite OR with 95% CI (OR: >999.999, 95% CI: 999.999 whereas there was a finite and consistent regression coefficient for gender (OR: 5.35; 95% CI: 0.42, 816.48 using PLR. After adjusting for all the confounding variables, hyponatremia entailed 7.9 (95% CI: 2.06, 38.86 times higher risk for the development of hiccups as was found using PLR whereas there was an overestimation of risk OR: 10.76 (95% CI: 2.17, 53.41 using the conventional method. Simulation experiment shows that the estimated coverage probability of this method is near the nominal level of 95% even for small sample sizes and for a large number of covariates. Conclusions: PLR is almost equal to the ordinary logistic regression when the sample size is large and is superior in small cell

Establishment of regression dependences. Linear and nonlinear dependences

International Nuclear Information System (INIS)

Onishchenko, A.M.

1994-01-01

The main problems of determination of linear and 19 types of nonlinear regression dependences are completely discussed. It is taken into consideration that total dispersions are the sum of measurement dispersions and parameter variation dispersions themselves. Approaches to all dispersions determination are described. It is shown that the least square fit gives inconsistent estimation for industrial objects and processes. The correction methods by taking into account comparable measurement errors for both variable give an opportunity to obtain consistent estimation for the regression equation parameters. The condition of the correction technique application expediency is given. The technique for determination of nonlinear regression dependences taking into account the dependence form and comparable errors of both variables is described. 6 refs., 1 tab

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

Science.gov (United States)

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

2010-01-15

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

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

Science.gov (United States)

Curs, Bradley R.; Harper, Casandra E.

2012-01-01

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

Reconstructing Regional Ionospheric Electron Density: A Combined Spherical Slepian Function and Empirical Orthogonal Function Approach

Science.gov (United States)

Farzaneh, Saeed; Forootan, Ehsan

2018-03-01

The computerized ionospheric tomography is a method for imaging the Earth's ionosphere using a sounding technique and computing the slant total electron content (STEC) values from data of the global positioning system (GPS). The most common approach for ionospheric tomography is the voxel-based model, in which (1) the ionosphere is divided into voxels, (2) the STEC is then measured along (many) satellite signal paths, and finally (3) an inversion procedure is applied to reconstruct the electron density distribution of the ionosphere. In this study, a computationally efficient approach is introduced, which improves the inversion procedure of step 3. Our proposed method combines the empirical orthogonal function and the spherical Slepian base functions to describe the vertical and horizontal distribution of electron density, respectively. Thus, it can be applied on regional and global case studies. Numerical application is demonstrated using the ground-based GPS data over South America. Our results are validated against ionospheric tomography obtained from the constellation observing system for meteorology, ionosphere, and climate (COSMIC) observations and the global ionosphere map estimated by international centers, as well as by comparison with STEC derived from independent GPS stations. Using the proposed approach, we find that while using 30 GPS measurements in South America, one can achieve comparable accuracy with those from COSMIC data within the reported accuracy (1 × 1011 el/cm3) of the product. Comparisons with real observations of two GPS stations indicate an absolute difference is less than 2 TECU (where 1 total electron content unit, TECU, is 1016 electrons/m2).

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.

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.

Eikonal approach to the atomic break-up process by polarized electrons

International Nuclear Information System (INIS)

Onaga, Tomohide

1992-01-01

The cross section asymmetry for ionization of hydrogen atoms by electron impact is analysed in the eikonal approach. A new formulation is given for the evaluation of the exchange amplitude up to higher partial Coulomb waves. It is concluded that the cross section asymmetry gives an important criterion or interesting test of validity of approximation methods with the exchange effect. (author)

Hybridization approach to in-line and off-axis (electron) holography for superior resolution and phase sensitivity

Science.gov (United States)

Ozsoy-Keskinbora, C.; Boothroyd, C. B.; Dunin-Borkowski, R. E.; van Aken, P. A.; Koch, C. T.

2014-01-01

Holography - originally developed for correcting spherical aberration in transmission electron microscopes - is now used in a wide range of disciplines that involve the propagation of waves, including light optics, electron microscopy, acoustics and seismology. In electron microscopy, the two primary modes of holography are Gabor's original in-line setup and an off-axis approach that was developed subsequently. These two techniques are highly complementary, offering superior phase sensitivity at high and low spatial resolution, respectively. All previous investigations have focused on improving each method individually. Here, we show how the two approaches can be combined in a synergetic fashion to provide phase information with excellent sensitivity across all spatial frequencies, low noise and an efficient use of electron dose. The principle is also expected to be widely to applications of holography in light optics, X-ray optics, acoustics, ultra-sound, terahertz imaging, etc. PMID:25387480

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)

Identifying predictors of physics item difficulty: A linear regression approach

Science.gov (United States)

Mesic, Vanes; Muratovic, Hasnija

2011-06-01

Large-scale assessments of student achievement in physics are often approached with an intention to discriminate students based on the attained level of their physics competencies. Therefore, for purposes of test design, it is important that items display an acceptable discriminatory behavior. To that end, it is recommended to avoid extraordinary difficult and very easy items. Knowing the factors that influence physics item difficulty makes it possible to model the item difficulty even before the first pilot study is conducted. Thus, by identifying predictors of physics item difficulty, we can improve the test-design process. Furthermore, we get additional qualitative feedback regarding the basic aspects of student cognitive achievement in physics that are directly responsible for the obtained, quantitative test results. In this study, we conducted a secondary analysis of data that came from two large-scale assessments of student physics achievement at the end of compulsory education in Bosnia and Herzegovina. Foremost, we explored the concept of “physics competence” and performed a content analysis of 123 physics items that were included within the above-mentioned assessments. Thereafter, an item database was created. Items were described by variables which reflect some basic cognitive aspects of physics competence. For each of the assessments, Rasch item difficulties were calculated in separate analyses. In order to make the item difficulties from different assessments comparable, a virtual test equating procedure had to be implemented. Finally, a regression model of physics item difficulty was created. It has been shown that 61.2% of item difficulty variance can be explained by factors which reflect the automaticity, complexity, and modality of the knowledge structure that is relevant for generating the most probable correct solution, as well as by the divergence of required thinking and interference effects between intuitive and formal physics knowledge

Identifying predictors of physics item difficulty: A linear regression approach

Directory of Open Access Journals (Sweden)

Hasnija Muratovic

2011-06-01

Full Text Available Large-scale assessments of student achievement in physics are often approached with an intention to discriminate students based on the attained level of their physics competencies. Therefore, for purposes of test design, it is important that items display an acceptable discriminatory behavior. To that end, it is recommended to avoid extraordinary difficult and very easy items. Knowing the factors that influence physics item difficulty makes it possible to model the item difficulty even before the first pilot study is conducted. Thus, by identifying predictors of physics item difficulty, we can improve the test-design process. Furthermore, we get additional qualitative feedback regarding the basic aspects of student cognitive achievement in physics that are directly responsible for the obtained, quantitative test results. In this study, we conducted a secondary analysis of data that came from two large-scale assessments of student physics achievement at the end of compulsory education in Bosnia and Herzegovina. Foremost, we explored the concept of “physics competence” and performed a content analysis of 123 physics items that were included within the above-mentioned assessments. Thereafter, an item database was created. Items were described by variables which reflect some basic cognitive aspects of physics competence. For each of the assessments, Rasch item difficulties were calculated in separate analyses. In order to make the item difficulties from different assessments comparable, a virtual test equating procedure had to be implemented. Finally, a regression model of physics item difficulty was created. It has been shown that 61.2% of item difficulty variance can be explained by factors which reflect the automaticity, complexity, and modality of the knowledge structure that is relevant for generating the most probable correct solution, as well as by the divergence of required thinking and interference effects between intuitive and formal

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA.

Science.gov (United States)

Heddam, Salim

2014-11-01

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

Science.gov (United States)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

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...

A real-space stochastic density matrix approach for density functional electronic structure.

Science.gov (United States)

Beck, Thomas L

2015-12-21

The recent development of real-space grid methods has led to more efficient, accurate, and adaptable approaches for large-scale electrostatics and density functional electronic structure modeling. With the incorporation of multiscale techniques, linear-scaling real-space solvers are possible for density functional problems if localized orbitals are used to represent the Kohn-Sham energy functional. These methods still suffer from high computational and storage overheads, however, due to extensive matrix operations related to the underlying wave function grid representation. In this paper, an alternative stochastic method is outlined that aims to solve directly for the one-electron density matrix in real space. In order to illustrate aspects of the method, model calculations are performed for simple one-dimensional problems that display some features of the more general problem, such as spatial nodes in the density matrix. This orbital-free approach may prove helpful considering a future involving increasingly parallel computing architectures. Its primary advantage is the near-locality of the random walks, allowing for simultaneous updates of the density matrix in different regions of space partitioned across the processors. In addition, it allows for testing and enforcement of the particle number and idempotency constraints through stabilization of a Feynman-Kac functional integral as opposed to the extensive matrix operations in traditional approaches.

Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

International Nuclear Information System (INIS)

Che Jinxing; Wang Jianzhou

2010-01-01

In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.

Nanotubule and Tour Molecule Based Molecular Electronics: Suggestion for a Hybrid Approach

Science.gov (United States)

Srivastava, Deepak; Saini, Subhash (Technical Monitor)

1998-01-01

Recent experimental and theoretical attempts and results indicate two distinct broad pathways towards future molecular electronic devices and architectures. The first is the approach via Tour type ladder molecules and their junctions which can be fabricated with solution phase chemical approaches. Second are fullerenes or nanotubules and their junctions which may have better conductance, switching and amplifying characteristics but can not be made through well controlled and defined chemical means. A hybrid approach combining the two pathways to take advantage of the characteristics of both is suggested. Dimension and scale of such devices would be somewhere in between isolated molecule and nanotubule based devices but it maybe possible to use self-assembly towards larger functional and logicalunits.

Consistent quantum approach to new laser-electron-nuclear effects in diatomic molecules

International Nuclear Information System (INIS)

Glushkov, A V; Malinovskaya, S V; Loboda, A V; Shpinareva, I M; Prepelitsa, G P

2006-01-01

We present a consistent, quantum approach to the calculation of electron-nuclear γ. spectra (set of vibrational and rotational satellites) for nuclei in diatomic molecules. The approach generelizes the well known Letokhov-Minogin model and is based on the Dunham model potential approximation for potential curves of diatomic molecules. The method is applied to the calculation of probabilities of the vibration-rotation-nuclear transitions in a case of emission and absorption spectrum for the nucleus 127 I (E γ (0) = 203 keV) linked with the molecule H 127 I

Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

Science.gov (United States)

Chatzis, Sotirios P; Andreou, Andreas S

2015-11-01

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

A Bayesian nonparametric approach to causal inference on quantiles.

Science.gov (United States)

Xu, Dandan; Daniels, Michael J; Winterstein, Almut G

2018-02-25

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

Science.gov (United States)

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have

A nonparametric approach to calculate critical micelle concentrations: the local polynomial regression method

Energy Technology Data Exchange (ETDEWEB)

Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)

2004-02-01

The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)

Correlated nuclear and electronic dynamics in photoionized systems studied by quantum and mixed quantum-classical approaches

International Nuclear Information System (INIS)

Li, Zheng

2014-09-01

The advent of free electron lasers and high harmonic sources enables the investigation of electronic and nuclear dynamics of molecules and solids with atomic spatial resolution and femtosecond/attosecond time resolution, using bright and ultrashort laser pulses of frequency from terahertz to hard x-ray range. With the help of ultrashort laser pulses, the nuclear and electronic dynamics can be initiated, monitored and actively controlled at the typical time scale in the femtosecond to attosecond realm. Meanwhile, theoretical tools are required to describe the underlying mechanism. This doctoral thesis focuses on the development of theoretical tools based on full quantum mechanical multiconfiguration time-dependent Hartree (MCTDH) and mixed quantum classical approaches, which can be applied to describe the dynamical behavior of gas phase molecules and strongly correlated solids in the presence of ultrashort laser pulses. In the first part of this thesis, the focus is on the motion of electron holes in gas phase molecular ions created by extreme ultraviolet (XUV) photoionization and watched by spectroscopic approaches. The XUV photons create electron-hole in the valence orbitals of molecules by photoionization, the electron hole, as a positively charged quasi-particle, can then interact with the nuclei and the rest of electrons, leading to coupled non-Born-Oppenheimer dynamics. I present our study on electron-hole relaxation dynamics in valence ionized molecular ions of moderate size, using quantum wave packet and mixed quantum-classical approaches, using photoionized [H + (H 2 O) n ] + molecular ion as example. We have shown that the coupled motion of the electron-hole and the nuclei can be mapped out with femtosecond resolution by core-level x-ray transient absorption spectroscopy. Furthermore, in specific cases, the XUV photon can create a coherent electron hole, that can maintain its coherence to time scales of ∝ 1 picosecond. Employing XUV pump - IR probe

A new non-invasive approach based on polyhexamethylene biguanide increases the regression rate of HPV infection

Directory of Open Access Journals (Sweden)

Gentile Antonio

2012-09-01

Full Text Available Abstract Background HPV infection is a worldwide problem strictly linked to the development of cervical cancer. Persistence of the infection is one of the main factors responsible for the invasive progression and women diagnosed with intraepithelial squamous lesions are referred for further assessment and surgical treatments which are prone to complications. Despite this, there are several reports on the spontaneous regression of the infection. This study was carried out to evaluate the effectiveness of a long term polyhexamethylene biguanide (PHMB-based local treatment in improving the viral clearance, reducing the time exposure to the infection and avoiding the complications associated with the invasive treatments currently available. Method 100 women diagnosed with HPV infection were randomly assigned to receive six months of treatment with a PHMB-based gynecological solution (Monogin®, Lo.Li. Pharma, Rome - Italy or to remain untreated for the same period of time. Results A greater number of patients, who received the treatment were cleared of the infection at the two time points of the study (three and six months compared to that of the control group. A significant difference in the regression rate (90% Monogin group vs 70% control group was observed at the end of the study highlighting the time-dependent ability of PHMB to interact with the infection progression. Conclusions The topic treatment with PHMB is a preliminary safe and promising approach for patients with detected HPV infection increasing the chance of clearance and avoiding the use of invasive treatments when not strictly necessary. Trial registration ClinicalTrials.gov Identifier NCT01571141

Poisson regression for modeling count and frequency outcomes in trauma research.

Science.gov (United States)

Gagnon, David R; Doron-LaMarca, Susan; Bell, Margret; O'Farrell, Timothy J; Taft, Casey T

2008-10-01

The authors describe how the Poisson regression method for analyzing count or frequency outcome variables can be applied in trauma studies. The outcome of interest in trauma research may represent a count of the number of incidents of behavior occurring in a given time interval, such as acts of physical aggression or substance abuse. Traditional linear regression approaches assume a normally distributed outcome variable with equal variances over the range of predictor variables, and may not be optimal for modeling count outcomes. An application of Poisson regression is presented using data from a study of intimate partner aggression among male patients in an alcohol treatment program and their female partners. Results of Poisson regression and linear regression models are compared.

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

ANALYSIS OF THEORETICAL AND METHODOLOGICAL APPROACHES TO DESIGN OF ELECTRONIC TEXTBOOKS FOR STUDENTS OF HIGHER AGRICULTURAL EDUCATIONAL INSTITUTIONS

Directory of Open Access Journals (Sweden)

Olena Yu. Balalaieva

2017-06-01

Full Text Available The article deals with theoretical and methodological approaches to the design of electronic textbook, in particular systems, competence, activity, personality oriented, technological one, that in complex reflect the general trends in the formation of a new educational paradigm, distinctive features of which lie in constructing the heuristic searching model of the learning process, focusing on developmental teaching, knowledge integration, skills development for the independent information search and processing, technification of the learning process. The approach in this study is used in a broad sense as a synthesis of the basic ideas, views, principles that determine the overall research strategy. The main provisions of modern approaches to design are not antagonistic, they should be applied in a complex, taking into account the advantages of each of them and leveling shortcomings for the development of optimal concept of electronic textbook. The model of electronic textbook designing and components of methodology for its using based on these approaches are described.

Determinants of Inequality in Cameroon: A Regression-Based ...

African Journals Online (AJOL)

This paper applies the regression-based inequality decomposition approach to explore determinants of income inequality in Cameroon using the 2007 Cameroon household consumption survey. The contribution of each source to measured income inequality is the sum of its weighted marginal contributions in all possible ...

A Dictionary Approach to Electron Backscatter Diffraction Indexing.

Science.gov (United States)

Chen, Yu H; Park, Se Un; Wei, Dennis; Newstadt, Greg; Jackson, Michael A; Simmons, Jeff P; De Graef, Marc; Hero, Alfred O

2015-06-01

We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel's neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises-Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.

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...

Detection of Differential Item Functioning with Nonlinear Regression: A Non-IRT Approach Accounting for Guessing

Czech Academy of Sciences Publication Activity Database

Drabinová, Adéla; Martinková, Patrícia

2017-01-01

Roč. 54, č. 4 (2017), s. 498-517 ISSN 0022-0655 R&D Projects: GA ČR GJ15-15856Y Institutional support: RVO:67985807 Keywords : differential item functioning * non-linear regression * logistic regression * item response theory Subject RIV: AM - Education OBOR OECD: Statistics and probability Impact factor: 0.979, year: 2016

Assessment of perfusion by dynamic contrast-enhanced imaging using a deconvolution approach based on regression and singular value decomposition.

Science.gov (United States)

Koh, T S; Wu, X Y; Cheong, L H; Lim, C C T

2004-12-01

The assessment of tissue perfusion by dynamic contrast-enhanced (DCE) imaging involves a deconvolution process. For analysis of DCE imaging data, we implemented a regression approach to select appropriate regularization parameters for deconvolution using the standard and generalized singular value decomposition methods. Monte Carlo simulation experiments were carried out to study the performance and to compare with other existing methods used for deconvolution analysis of DCE imaging data. The present approach is found to be robust and reliable at the levels of noise commonly encountered in DCE imaging, and for different models of the underlying tissue vasculature. The advantages of the present method, as compared with previous methods, include its efficiency of computation, ability to achieve adequate regularization to reproduce less noisy solutions, and that it does not require prior knowledge of the noise condition. The proposed method is applied on actual patient study cases with brain tumors and ischemic stroke, to illustrate its applicability as a clinical tool for diagnosis and assessment of treatment response.

Pseudogap in the Eliashberg approach based on electron-phonon and electron-electron-phonon interaction

Energy Technology Data Exchange (ETDEWEB)

Szczesniak, R. [Institute of Physics, Czestochowa University of Technology (Poland); Institute of Physics, Jan Dlugosz University in Czestochowa (Poland); Durajski, A.P.; Duda, A.M. [Institute of Physics, Czestochowa University of Technology (Poland)

2017-04-15

The properties of the superconducting and the anomalous normal state were described by using the Eliashberg method. The pairing mechanism was reproduced with the help of the Hamiltonian, which models the electron-phonon and the electron-electron-phonon interaction (EEPh). The set of the Eliashberg equations, which determines the order parameter function (φ), the wave function renormalization factor (Z), and the energy shift function (χ), was derived. It was proven that for the sufficiently large values of the EEPh potential, the doping dependence of the order parameter (φ/Z) has the analogous course to that observed experimentally in cuprates. The energy gap in the electron density of states is induced by Z and χ - the contribution from φ is negligible. The electron density of states possesses the characteristic asymmetric form and the pseudogap is observed above the critical temperature. (copyright 2017 by WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

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

International Nuclear Information System (INIS)

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

2008-01-01

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

Real-space local polynomial basis for solid-state electronic-structure calculations: A finite-element approach

International Nuclear Information System (INIS)

Pask, J.E.; Klein, B.M.; Fong, C.Y.; Sterne, P.A.

1999-01-01

We present an approach to solid-state electronic-structure calculations based on the finite-element method. In this method, the basis functions are strictly local, piecewise polynomials. Because the basis is composed of polynomials, the method is completely general and its convergence can be controlled systematically. Because the basis functions are strictly local in real space, the method allows for variable resolution in real space; produces sparse, structured matrices, enabling the effective use of iterative solution methods; and is well suited to parallel implementation. The method thus combines the significant advantages of both real-space-grid and basis-oriented approaches and so promises to be particularly well suited for large, accurate ab initio calculations. We develop the theory of our approach in detail, discuss advantages and disadvantages, and report initial results, including electronic band structures and details of the convergence of the method. copyright 1999 The American Physical Society

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

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

2009-01-01

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

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

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

2010-01-01

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

Development of a User Interface for a Regression Analysis Software Tool

Science.gov (United States)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

Multiple Response Regression for Gaussian Mixture Models with Known Labels.

Science.gov (United States)

Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

2012-12-01

Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.

On concurvity in nonlinear and nonparametric regression models

Directory of Open Access Journals (Sweden)

Sonia Amodio

2014-12-01

Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.

Testing for marginal linear effects in quantile regression

KAUST Repository

Wang, Huixia Judy

2017-10-23

The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.

Testing for marginal linear effects in quantile regression

KAUST Repository

Wang, Huixia Judy; McKeague, Ian W.; Qian, Min

2017-01-01

The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.

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

Science.gov (United States)

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

2012-01-01

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

The effect of foreign aid on corruption: A quantile regression approach

OpenAIRE

Okada, Keisuke; Samreth, Sovannroeun

2011-01-01

This paper investigates the effect of foreign aid on corruption using a quantile regression method. Our estimation results illustrate that foreign aid generally lessens corruption and, in particular, its reduction effect is larger in countries with low levels of corruption. In addition, considering foreign aid by donors, our analysis indicates that while multilateral aid has a larger reduction impact on corruption, bilateral aid from the world’s leading donors, such as France, the United King...

Hot electrons and the approach to metallic behavior in Kx(KCl)1-x

NARCIS (Netherlands)

Silvestrelli, P.L.; Alavi, A.; Parrinello, M.; Frenkel, D.

1996-01-01

The approach to the metallic phase of molten Kx(KCl)1-x mixtures is studied using ab initio molecular dynamics based on finite-temperature density functional theory. The finite electronic temperature is found to result in new and unexpected effects. In particular, we observe a thermally induced

Linear-algebraic approach to electron-molecule collisions: General formulation

International Nuclear Information System (INIS)

Collins, L.A.; Schneider, B.I.

1981-01-01

We present a linear-algebraic approach to electron-molecule collisions based on an integral equations form with either logarithmic or asymptotic boundary conditions. The introduction of exchange effects does not alter the basic form or order of the linear-algebraic equations for a local potential. In addition to the standard procedure of directly evaluating the exchange integrals by numerical quadrature, we also incorporate exchange effects through a separable-potential approximation. Efficient schemes are developed for reducing the number of points and channels that must be included. The method is applied at the static-exchange level to a number of molecular systems including H 2 , N 2 , LiH, and CO 2

A new approach in the development of quality management systems for (micro)electronics

Science.gov (United States)

Bacivarov, Ioan C.; Bacivarov, Angelica; Gherghina, Cǎtǎlina

2016-12-01

This paper presents the new approach in the analysis of the Quality Management Systems (QMS) of companies, based on the revised standard ISO 9001:2015. In the first part of the paper, QMS based on ISO 9001 certification are introduced; the changes and the updates proposed for the new version of ISO 9001:2015 are critically analyzed, based on the documents elaborated by ISO/TC 176. The approach based on ISO 9001:2015 could be considered as "beginning of a new era in development of quality management systems". A comparison between the between the "old" standard ISO 9001:2008 and the "new" standard ISO 9001:2015 is made. In the second part of the paper, steps to be followed in a company to implement this new standard are presented. A peculiar attention is given to the new concept of risk-based thinking in order to support and improve application of the process based approach. The authors conclude that, by considering risk throughout the organization the likelihood of achieving stated objectives is improved, output is more consistent and customers can be confident that they will receive the expected results. Finally, the benefits of the new approach in the development of quality management systems are outlined, as well as how they are reflected in the management of companies in general and those in electronics field, in particular. As demonstrated in this paper, well understood and properly applied, the new approach based on the revised standard ISO9001:2015 could offer a better quality management for companies operating in electronics and beyond.

Electronic computer prediction of properties of binary refractory transition metal compounds on the base of their simplificated electronic structure

International Nuclear Information System (INIS)

Kutolin, S.A.; Kotyukov, V.I.

1979-01-01

An attempt is made to obtain calculation equations of macroscopic physico-chemical properties of transition metal refractory compounds (density, melting temperature, Debye characteristic temperature, microhardness, standard formation enthalpy, thermo-emf) using the method of the regression analysis. Apart from the compound composition the argument of the regression equation is the distribution of electron bands of d-transition metals, created by the energy electron distribution in the simplified zone structure of transition metals and approximated by Chebishev polynoms, by the position of Fermi energy on the map of distribution of electron band energy depending upon the value of quasi-impulse, multiple to the first, second and third Brillouin zone for transition metals. The maximum relative error of the regressions obtained as compared with the literary data is 15-20 rel.%

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...

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

DEFF Research Database (Denmark)

Ambrogi, Federico; Scheike, Thomas H.

2016-01-01

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

A practical approach for electron monitor unit calculation

International Nuclear Information System (INIS)

Choi, David; Patyal, Baldev; Cho, Jongmin; Cheng, Ing Y; Nookala, Prashanth

2009-01-01

Electron monitor unit (MU) calculation requires measured beam data such as the relative output factor (ROF) of a cone, insert correction factor (ICF) and effective source-to-surface distance (ESD). Measuring the beam data to cover all possible clinical cases is not practical for a busy clinic because it takes tremendous time and labor. In this study, we propose a practical approach to reduce the number of data measurements without affecting accuracy. It is based on two findings of dosimetric properties of electron beams. One is that the output ratio of two inserts is independent of the cone used, and the other is that ESD is a function of field size but independent of cone and jaw opening. For the measurements to prove the findings, a parallel plate ion chamber (Markus, PTW 23343) with an electrometer (Cardinal Health 35040) was used. We measured the outputs to determine ROF, ICF and ESD of different energies (5-21 MeV). Measurements were made in a Plastic Water(TM) phantom or in water. Three linear accelerators were used: Siemens MD2 (S/N 2689), Siemens Primus (S/N 3305) and Varian Clinic 21-EX (S/N 1495). With these findings, the number of data set to be measured can be reduced to less than 20% of the data points. (note)

Direct electron transfer: an approach for electrochemical biosensors with higher selectivity and sensitivity

Directory of Open Access Journals (Sweden)

Freire Renato S.

2003-01-01

Full Text Available The most promising approach for the development of electrochemical biosensors is to establish a direct electrical communication between the biomolecules and the electrode surface. This review focuses on advances, directions and strategies in the development of third generation electrochemical biosensors. Subjects covered include a brief description of the fundamentals of the electron transfer phenomenon and amperometric biosensor development (different types and new oriented enzyme immobilization techniques. Special attention is given to different redox enzymes and proteins capable of electrocatalyzing reactions via direct electron transfer. The analytical applications and future trends for third generation biosensors are also presented and discussed.

Logistic regression for risk factor modelling in stuttering research.

Science.gov (United States)

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

Science.gov (United States)

Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of

A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning

International Nuclear Information System (INIS)

Gudur, Madhu Sudhan Reddy; Hara, Wendy; Le, Quynh-Thu; Wang, Lei; Xing, Lei; Li, Ruijiang

2014-01-01

MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm’s accuracy of electron density mapping and its ability to detect bone in the head for eight patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as receiver operating characteristics for bone detection (HU > 200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p = 2  ×  10 −4 ), 283 for the intensity approach (p = 2  ×  10 −6 ) and 282

Top Incomes, Heavy Tails, and Rank-Size Regressions

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Christian Schluter

2018-03-01

Full Text Available In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading parametric income models (so the upper income tail is less heavy than estimated, which leads to test size distortions and undermines inference. For practical work, we propose a sensitivity analysis based on regression diagnostics in order to assess the likely impact of the distortion. The methods are illustrated using data on top incomes in the UK.

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

Science.gov (United States)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

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

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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.

Healthcare Expenditures Associated with Depression Among Individuals with Osteoarthritis: Post-Regression Linear Decomposition Approach.

Science.gov (United States)

Agarwal, Parul; Sambamoorthi, Usha

2015-12-01

Depression is common among individuals with osteoarthritis and leads to increased healthcare burden. The objective of this study was to examine excess total healthcare expenditures associated with depression among individuals with osteoarthritis in the US. Adults with self-reported osteoarthritis (n = 1881) were identified using data from the 2010 Medical Expenditure Panel Survey (MEPS). Among those with osteoarthritis, chi-square tests and ordinary least square regressions (OLS) were used to examine differences in healthcare expenditures between those with and without depression. Post-regression linear decomposition technique was used to estimate the relative contribution of different constructs of the Anderson's behavioral model, i.e., predisposing, enabling, need, personal healthcare practices, and external environment factors, to the excess expenditures associated with depression among individuals with osteoarthritis. All analysis accounted for the complex survey design of MEPS. Depression coexisted among 20.6 % of adults with osteoarthritis. The average total healthcare expenditures were $13,684 among adults with depression compared to $9284 among those without depression. Multivariable OLS regression revealed that adults with depression had 38.8 % higher healthcare expenditures (p regression linear decomposition analysis indicated that 50 % of differences in expenditures among adults with and without depression can be explained by differences in need factors. Among individuals with coexisting osteoarthritis and depression, excess healthcare expenditures associated with depression were mainly due to comorbid anxiety, chronic conditions and poor health status. These expenditures may potentially be reduced by providing timely intervention for need factors or by providing care under a collaborative care model.

Virtual machine consolidation enhancement using hybrid regression algorithms

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Amany Abdelsamea

2017-11-01

Full Text Available Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. VM consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/underload detection, VM selection and VM placement. Most of the current VM consolidation approaches apply either heuristic-based techniques, such as static utilization thresholds, decision-making based on statistical analysis of historical data; or simply periodic adaptation of the VM allocation. Most of those algorithms rely on CPU utilization only for host overload detection. In this paper we propose using hybrid factors to enhance VM consolidation. Specifically we developed a multiple regression algorithm that uses CPU utilization, memory utilization and bandwidth utilization for host overload detection. The proposed algorithm, Multiple Regression Host Overload Detection (MRHOD, significantly reduces energy consumption while ensuring a high level of adherence to Service Level Agreements (SLA since it gives a real indication of host utilization based on three parameters (CPU, Memory, Bandwidth utilizations instead of one parameter only (CPU utilization. Through simulations we show that our approach reduces power consumption by 6 times compared to single factor algorithms using random workload. Also using PlanetLab workload traces we show that MRHOD improves

Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

Science.gov (United States)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-06-01

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

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.

The effect of different electrodes on the electronic transmission of benzene junctions: Analytical approach

Energy Technology Data Exchange (ETDEWEB)

Mohebbi, Razie; Seyed-Yazdi, Jamileh, E-mail: j.seyedyazdi@vru.ac.ir

2016-06-01

In this paper we have investigated the electronic transmission of systems electrode–benzene–electrode using the Landauer approach. The effect of different electrodes made of metal (Au) and semiconductors (Si, TiO{sub 2}) is investigated. These three electrodes are compared between them and the results show that the electronic transmission of benzene junctions, when using semiconductor electrodes, is associated to a gap in transmission which is due to the electrodes band gap. As a consequence, a threshold voltage is necessary to obtain conducting channels.

Easy methods for extracting individual regression slopes: Comparing SPSS, R, and Excel

Directory of Open Access Journals (Sweden)

Roland Pfister

2013-10-01

Full Text Available Three different methods for extracting coefficientsof linear regression analyses are presented. The focus is on automatic and easy-to-use approaches for common statistical packages: SPSS, R, and MS Excel / LibreOffice Calc. Hands-on examples are included for each analysis, followed by a brief description of how a subsequent regression coefficient analysis is performed.

Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

Science.gov (United States)

Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

2018-02-01

A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

Boosting structured additive quantile regression for longitudinal childhood obesity data.

Science.gov (United States)

Fenske, Nora; Fahrmeir, Ludwig; Hothorn, Torsten; Rzehak, Peter; Höhle, Michael

2013-07-25

Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach

Energy Technology Data Exchange (ETDEWEB)

Ren, Shangjie [Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin (China); Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States); Hara, Wendy; Wang, Lei; Buyyounouski, Mark K.; Le, Quynh-Thu; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States); Li, Ruijiang, E-mail: rli2@stanford.edu [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States)

2017-03-15

Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.

A Cost-Effective Approach for Migrating Enterprise Electronic Mail Systems

Directory of Open Access Journals (Sweden)

Emmanuel Omojokun

2008-02-01

Full Text Available Electronic mail (E-mail is one of the most utilized application software systems in modern-day organizations. The major messaging application programs used in the enterprise are IBM Lotus Notes also known as Domino, Microsoft Exchange Servers, and Novel GroupWise. For various reasons – such as high cost of maintenance, undeliverable e-mail issue and loss of attachments, companies find it necessary to either migrate to newer versions of their messaging software or to an entirely different software. In either case, the process must be carefully planned, well designed and properly implemented to avoid disaster. In this paper, we present a cost-effective approach for migrating a particular messaging software. The approach was implemented and tested for the migration of GroupWise 5.5 to Exchange Server 2003. We present our success story and lessons learned from the case. A six-week and one-year post migration system-audits indicated that the organization derived several benefits including significant cost savings as a result of this particular approach. Chief information/technology officers and e-mail administrators will benefit immensely from the "best practice" strategy hereby presented.

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

Directory of Open Access Journals (Sweden)

Yoonseok Shin

2015-01-01

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

A linear regression approach to evaluate the green supply chain management impact on industrial organizational performance.

Science.gov (United States)

Mumtaz, Ubaidullah; Ali, Yousaf; Petrillo, Antonella

2018-05-15

The increase in the environmental pollution is one of the most important topic in today's world. In this context, the industrial activities can pose a significant threat to the environment. To manage problems associate to industrial activities several methods, techniques and approaches have been developed. Green supply chain management (GSCM) is considered one of the most important "environmental management approach". In developing countries such as Pakistan the implementation of GSCM practices is still in its initial stages. Lack of knowledge about its effects on economic performance is the reason because of industries fear to implement these practices. The aim of this research is to perceive the effects of GSCM practices on organizational performance in Pakistan. In this research the GSCM practices considered are: internal practices, external practices, investment recovery and eco-design. While, the performance parameters considered are: environmental pollution, operational cost and organizational flexibility. A set of hypothesis propose the effect of each GSCM practice on the performance parameters. Factor analysis and linear regression are used to analyze the survey data of Pakistani industries, in order to authenticate these hypotheses. The findings of this research indicate a decrease in environmental pollution and operational cost with the implementation of GSCM practices, whereas organizational flexibility has not improved for Pakistani industries. These results aim to help managers regarding their decision of implementing GSCM practices in the industrial sector of Pakistan. Copyright © 2017 Elsevier B.V. All rights reserved.

Electron-impact ionization of oriented molecules using the time-dependent close-coupling approach

Energy Technology Data Exchange (ETDEWEB)

Colgan, J [Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Pindzola, M S, E-mail: jcolgan@lanl.gov [Department of Physics, Auburn University, Auburn, AL 36849 (United States)

2011-04-01

An overview is given on recent progress on computing triple differential cross sections for electron-impact ionization of the hydrogen molecule using a time-dependent close-coupling approach. Our calculations, when averaged over all molecular orientations, are generally in very good agreement with (e,2e) measurements made on H{sub 2}, where the molecular orientation is unknown, for a range of incident energies and outgoing electron angles and energies. In this paper, we present TDCS for ionization of H{sub 2} at specific molecular orientations. It is hoped that this study will help stimulate future measurements of TDCS from oriented H{sub 2} at medium impact energies.

Problems and Projects Based Approach For Analog Electronic Circuits' Course

Directory of Open Access Journals (Sweden)

Vahé Nerguizian

2009-04-01

Full Text Available New educational methods and approaches are recently introduced and implemented at several North American and European universities using Problems and Projects Based Approach (PPBA. The PPBA employs a teaching technique based mostly on competences/skills rather than only on knowledge. This method has been implemented and proven by several pedagogical instructors and authors at several educational institutions. This approach is used at different disciplines such as medicine, biology, engineering and many others. It has the advantage to improve the student's skills and the knowledge retention rate, and reflects the 21st century industrial/company needs and demands. Before implementing this approach to a course, a good resources preparation and planning is needed upfront by the responsible or instructor of the course to achieve the course and students related objectives. This paper presents the preparation, the generated documentation and the implementation of a pilot project utilizing PPBA education for a second year undergraduate electronic course over a complete semester, and for two different class groups (morning and evening groups. The outcome of this project (achieved goals, observed difficulties and lessons learned is presented based on different tools such as students 'in class' communication and feedback, different course evaluation forms and the professor/instructor feedback. Resources, challenges, difficulties and recommendations are also assessed and presented. The impact, the effect and the results (during and at the end of the academic fall session of the PPBA on students and instructor are discussed, validated, managed and communicated to help other instructor in taking appropriate approach decisions with respect to this new educational approach compared to the classical one.

A New Approach in Teaching Power Electronics Control of Electrical Drives using Real-Time

DEFF Research Database (Denmark)

Teodorescu, Remus; Bech, Michael Møller; Blaabjerg, Frede

2000-01-01

A new approach in teaching power electronics and electrical drives is achieved at the Flexible Drives System Laboratory (FDSL) from Aalborg University by using the new Total Development Environment (TDE) concept that allows a full visual block-oriented programming of dynamic real-time systems...

Prediction of radiation levels in residences: A methodological comparison of CART [Classification and Regression Tree Analysis] and conventional regression

International Nuclear Information System (INIS)

Janssen, I.; Stebbings, J.H.

1990-01-01

In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs

Canonical variate regression.

Science.gov (United States)

Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun

2016-07-01

In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Classifying machinery condition using oil samples and binary logistic regression

Science.gov (United States)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

Real-time regression analysis with deep convolutional neural networks

OpenAIRE

Huerta, E. A.; George, Daniel; Zhao, Zhizhen; Allen, Gabrielle

2018-01-01

We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.

Neighborhood Effects in Wind Farm Performance: A Regression Approach

Directory of Open Access Journals (Sweden)

Matthias Ritter

2017-03-01

Full Text Available The optimization of turbine density in wind farms entails a trade-off between the usage of scarce, expensive land and power losses through turbine wake effects. A quantification and prediction of the wake effect, however, is challenging because of the complex aerodynamic nature of the interdependencies of turbines. In this paper, we propose a parsimonious data driven regression wake model that can be used to predict production losses of existing and potential wind farms. Motivated by simple engineering wake models, the predicting variables are wind speed, the turbine alignment angle, and distance. By utilizing data from two wind farms in Germany, we show that our models can compete with the standard Jensen model in predicting wake effect losses. A scenario analysis reveals that a distance between turbines can be reduced by up to three times the rotor size, without entailing substantial production losses. In contrast, an unfavorable configuration of turbines with respect to the main wind direction can result in production losses that are much higher than in an optimal case.

Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.

Science.gov (United States)

Ohring, G.

1972-01-01

Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.

Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements

KAUST Repository

Ryu, Duchwan

2010-09-28

We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.

The electronic structure of molecules by a many-body approach. Pt. 1

International Nuclear Information System (INIS)

Niessen, W. von; Cederbaum, L.S.; Kraemer, W.P.

1976-01-01

The ionization potentials of benzene are studied by an ab initio many-body approach which includes the effects of electron correlation and reorganization beyond the one-particle approximation. The calculations confirm the assignment of the photoelectron spectrum experimentally proposed by Jonsson and Lindholm: 1esub(1g)(π), 2esub(2g), 1asub(2u)(π), 2esub(1u), 1bsub(2u), 1bsub(1u), 2asub(1g), 1esub(2g) in order of increasing binding energy. To definitely establish the ordering of the ionization potentials in the second band, which has been very controversial, the corresponding vibrational structure has been calculated. A number of one-electron properties are calculated in the one-particle approximation and compared to experimental work and other theoretical calculations. (orig.) [de

Bayesian Inference of a Multivariate Regression Model

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Marick S. Sinay

2014-01-01

Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

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

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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.

Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

Directory of Open Access Journals (Sweden)

Yuehjen E. Shao

2013-01-01

Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

Compact femtosecond electron diffractometer with 100 keV electron bunches approaching the single-electron pulse duration limit

International Nuclear Information System (INIS)

Waldecker, Lutz; Bertoni, Roman; Ernstorfer, Ralph

2015-01-01

We present the design and implementation of a highly compact femtosecond electron diffractometer working at electron energies up to 100 keV. We use a multi-body particle tracing code to simulate electron bunch propagation through the setup and to calculate pulse durations at the sample position. Our simulations show that electron bunches containing few thousands of electrons per bunch are only weakly broadened by space-charge effects and their pulse duration is thus close to the one of a single-electron wavepacket. With our compact setup, we can create electron bunches containing up to 5000 electrons with a pulse duration below 100 fs on the sample. We use the diffractometer to track the energy transfer from photoexcited electrons to the lattice in a thin film of titanium. This process takes place on the timescale of few-hundred femtoseconds and a fully equilibrated state is reached within 1 ps

Shaping the Electronic Library--The UW-Madison Approach.

Science.gov (United States)

Dean, Charles W., Ed.; Frazier, Ken; Pope, Nolan F.; Gorman, Peter C.; Dentinger, Sue; Boston, Jeanne; Phillips, Hugh; Daggett, Steven C.; Lundquist, Mitch; McClung, Mark; Riley, Curran; Allan, Craig; Waugh, David

1998-01-01

This special theme section describes the University of Wisconsin-Madison's experience building its Electronic Library. Highlights include integrating resources and services; the administrative framework; the public electronic library, including electronic publishing capability and access to World Wide Web-based and other electronic resources;…

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

Science.gov (United States)

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors

Directory of Open Access Journals (Sweden)

Xibin Zhang

2016-04-01

Full Text Available This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP growth rates among the organisation for economic co-operation and development (OECD and non-OECD countries.

Electronics lab instructors' approaches to troubleshooting instruction

Science.gov (United States)

Dounas-Frazer, Dimitri R.; Lewandowski, H. J.

2017-06-01

In this exploratory qualitative study, we describe instructors' self-reported practices for teaching and assessing students' ability to troubleshoot in electronics lab courses. We collected audio data from interviews with 20 electronics instructors from 18 institutions that varied by size, selectivity, and other factors. In addition to describing participants' instructional practices, we characterize their perceptions about the role of troubleshooting in electronics, the importance of the ability to troubleshoot more generally, and what it means for students to be competent troubleshooters. One major finding of this work is that, while almost all instructors in our study said that troubleshooting is an important learning outcome for students in electronics lab courses, only half of instructors said they directly assessed students' ability to troubleshoot. Based on our findings, we argue that there is a need for research-based instructional materials that attend to both cognitive and noncognitive aspects of troubleshooting proficiency. We also identify several areas for future investigation related to troubleshooting instruction in electronics lab courses.

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 bioelectrochemical approach to characterize extracellular electron transfer by Synechocystis sp. PCC6803.

Directory of Open Access Journals (Sweden)

Angelo Cereda

Full Text Available Biophotovoltaic devices employ photosynthetic organisms at the anode of a microbial fuel cell to generate electrical power. Although a range of cyanobacteria and algae have been shown to generate photocurrent in devices of a multitude of architectures, mechanistic understanding of extracellular electron transfer by phototrophs remains minimal. Here we describe a mediatorless bioelectrochemical device to measure the electrogenic output of a planktonically grown cyanobacterium, Synechocystis sp. PCC6803. Light dependent production of current is measured, and its magnitude is shown to scale with microbial cell concentration and light intensity. Bioelectrochemical characterization of a Synechocystis mutant lacking Photosystem II demonstrates conclusively that production of the majority of photocurrent requires a functional water splitting aparatus and electrons are likely ultimately derived from water. This shows the potential of the device to rapidly and quantitatively characterize photocurrent production by genetically modified strains, an approach that can be used in future studies to delineate the mechanisms of cyanobacterial extracellular electron transport.

An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy

DEFF Research Database (Denmark)

Merlo, Juan; Wagner, Philippe; Ghith, Nermin

2016-01-01

BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that disting...

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....

Primary processes of the electron-protic species coupling in pure aqueous phases: - femtosecond laser spectroscopy study; - quantum approach of the electron-water interaction

International Nuclear Information System (INIS)

Pommeret, Stanislas

1991-01-01

This thesis work deals with the coupling mechanisms between an electron, water molecules or protic species (hydronium ion, hydroxyl radical). Two complementary studies have been carry out in pure aqueous phases. The first one is concerned with the structural aspect of the hydrated electron which is studied via a semi-quantum approach Splitting Operator Method. The results indicates the importance of the second hydration shell in the localisation of an electron at 77 and 300 Kelvin. The second part of this work relates to the dynamic of the primary processes in light or heavy water at room temperature: the ion-molecule reaction, radical pair formation, geminate recombination of the hydrated electron with the hydronium ion and the hydroxyl radical. The dynamic of these reactions is studied by time resolved absorption spectroscopy from the near infrared to the near ultraviolet with a few tens femto-seconds temporal precision. The analysis of the primary processes takes into account the protic properties of water molecules. (author) [fr

Meta-analytical synthesis of regression coefficients under different categorization scheme of continuous covariates.

Science.gov (United States)

Yoneoka, Daisuke; Henmi, Masayuki

2017-11-30

Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts. Copyright © 2017 John Wiley & Sons, Ltd.

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

Data.gov (United States)

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

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Science.gov (United States)

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

A Study on Technology Architecture and Serving Approaches of Electronic Government System

Science.gov (United States)

Liu, Chunnian; Huang, Yiyun; Pan, Qin

As E-government becomes a very active research area, a lot of solutions to solve citizens' needs are being deployed. This paper provides technology architecture of E-government system and approaches of service in Public Administrations. The proposed electronic system addresses the basic E-government requirements of user friendliness, security, interoperability, transparency and effectiveness in the communication between small and medium sized public organizations and their citizens, businesses and other public organizations. The paper has provided several serving approaches of E-government, which includes SOA, web service, mobile E-government, public library and every has its own characteristics and application scenes. Still, there are a number of E-government issues for further research on organization structure change, including research methodology, data collection analysis, etc.

Efficient and robust cell detection: A structured regression approach.

Science.gov (United States)

Xie, Yuanpu; Xing, Fuyong; Shi, Xiaoshuang; Kong, Xiangfei; Su, Hai; Yang, Lin

2018-02-01

Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and large variations in cell sizes and shapes. In addition, the ever-increasing amount of available datasets and the high resolution of whole-slice scanned images pose a further demand for efficient processing algorithms. In this paper, we present a novel structured regression model based on a proposed fully residual convolutional neural network for efficient cell detection. For each testing image, our model learns to produce a dense proximity map that exhibits higher responses at locations near cell centers. Our method only requires a few training images with weak annotations (just one dot indicating the cell centroids). We have extensively evaluated our method using four different datasets, covering different microscopy staining methods (e.g., H & E or Ki-67 staining) or image acquisition techniques (e.g., bright-filed image or phase contrast). Experimental results demonstrate the superiority of our method over existing state of the art methods in terms of both detection accuracy and running time. Copyright © 2017. Published by Elsevier B.V.

A LATENT CLASS POISSON REGRESSION-MODEL FOR HETEROGENEOUS COUNT DATA

NARCIS (Netherlands)

WEDEL, M; DESARBO, WS; BULT, [No Value; RAMASWAMY, [No Value

1993-01-01

In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing

Preference learning with evolutionary Multivariate Adaptive Regression Spline model

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll

2015-01-01

This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...

Predicting Performance on MOOC Assessments using Multi-Regression Models

OpenAIRE

Ren, Zhiyun; Rangwala, Huzefa; Johri, Aditya

2016-01-01

The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempt...

A logistic regression approach to model the willingness of consumers to adopt renewable energy sources

Science.gov (United States)

Ulkhaq, M. M.; Widodo, A. K.; Yulianto, M. F. A.; Widhiyaningrum; Mustikasari, A.; Akshinta, P. Y.

2018-03-01

The implementation of renewable energy in this globalization era is inevitable since the non-renewable energy leads to climate change and global warming; hence, it does harm the environment and human life. However, in the developing countries, such as Indonesia, the implementation of the renewable energy sources does face technical and social problems. For the latter, renewable energy sources implementation is only effective if the public is aware of its benefits. This research tried to identify the determinants that influence consumers’ intention in adopting renewable energy sources. In addition, this research also tried to predict the consumers who are willing to apply the renewable energy sources in their houses using a logistic regression approach. A case study was conducted in Semarang, Indonesia. The result showed that only eight variables (from fifteen) that are significant statistically, i.e., educational background, employment status, income per month, average electricity cost per month, certainty about the efficiency of renewable energy project, relatives’ influence to adopt the renewable energy sources, energy tax deduction, and the condition of the price of the non-renewable energy sources. The finding of this study could be used as a basis for the government to set up a policy towards an implementation of the renewable energy sources.

A practical approach to temperature effects in dissociative electron attachment cross sections using local complex potential theory

International Nuclear Information System (INIS)

Sugioka, Yuji; Takayanagi, Toshiyuki

2012-01-01

Highlights: ► Dissociative electron attachment cross sections for polyatomic molecules are calculated by a simple theoretical approach. ► Temperature effects can be reasonably reproduced with the present model. ► All the degrees-of-freedom are taken into account in the present dynamics approach. -- Abstract: We propose a practical computational scheme to obtain temperature dependence of dissociative electron attachment cross sections to polyatomic molecules within a local complex potential theory formalism. First we perform quantum path-integral molecular dynamics simulations on the potential energy surface for the neutral molecule in order to sample initial nuclear configurations as well as momenta. Classical trajectories are subsequently integrated on the potential energy surface for the anionic state and survival probabilities are simultaneously calculated along the obtained trajectories. We have applied this simple scheme to dissociative electron attachment processes to H 2 O and CF 3 Cl, for which several previous studies are available from both the experimental and theoretical sides.

A practical approach to temperature effects in dissociative electron attachment cross sections using local complex potential theory

Energy Technology Data Exchange (ETDEWEB)

Sugioka, Yuji [Department of Chemistry, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama 338-8570 (Japan); Takayanagi, Toshiyuki, E-mail: tako@mail.saitama-u.ac.jp [Department of Chemistry, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama 338-8570 (Japan)

2012-09-11

Highlights: Black-Right-Pointing-Pointer Dissociative electron attachment cross sections for polyatomic molecules are calculated by a simple theoretical approach. Black-Right-Pointing-Pointer Temperature effects can be reasonably reproduced with the present model. Black-Right-Pointing-Pointer All the degrees-of-freedom are taken into account in the present dynamics approach. -- Abstract: We propose a practical computational scheme to obtain temperature dependence of dissociative electron attachment cross sections to polyatomic molecules within a local complex potential theory formalism. First we perform quantum path-integral molecular dynamics simulations on the potential energy surface for the neutral molecule in order to sample initial nuclear configurations as well as momenta. Classical trajectories are subsequently integrated on the potential energy surface for the anionic state and survival probabilities are simultaneously calculated along the obtained trajectories. We have applied this simple scheme to dissociative electron attachment processes to H{sub 2}O and CF{sub 3}Cl, for which several previous studies are available from both the experimental and theoretical sides.

Multivariate nonparametric regression and visualization with R and applications to finance

CERN Document Server

Klemelä, Jussi

2014-01-01

A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functio

Independent variable complexity for regional regression of the flow duration curve in ungauged basins

Science.gov (United States)

Fouad, Geoffrey; Skupin, André; Hope, Allen

2016-04-01

The flow duration curve (FDC) is one of the most widely used tools to quantify streamflow. Its percentile flows are often required for water resource applications, but these values must be predicted for ungauged basins with insufficient or no streamflow data. Regional regression is a commonly used approach for predicting percentile flows that involves identifying hydrologic regions and calibrating regression models to each region. The independent variables used to describe the physiographic and climatic setting of the basins are a critical component of regional regression, yet few studies have investigated their effect on resulting predictions. In this study, the complexity of the independent variables needed for regional regression is investigated. Different levels of variable complexity are applied for a regional regression consisting of 918 basins in the US. Both the hydrologic regions and regression models are determined according to the different sets of variables, and the accuracy of resulting predictions is assessed. The different sets of variables include (1) a simple set of three variables strongly tied to the FDC (mean annual precipitation, potential evapotranspiration, and baseflow index), (2) a traditional set of variables describing the average physiographic and climatic conditions of the basins, and (3) a more complex set of variables extending the traditional variables to include statistics describing the distribution of physiographic data and temporal components of climatic data. The latter set of variables is not typically used in regional regression, and is evaluated for its potential to predict percentile flows. The simplest set of only three variables performed similarly to the other more complex sets of variables. Traditional variables used to describe climate, topography, and soil offered little more to the predictions, and the experimental set of variables describing the distribution of basin data in more detail did not improve predictions

An analytic approach to 2D electronic PE spectra of molecular systems

International Nuclear Information System (INIS)

Szoecs, V.

2011-01-01

Graphical abstract: The three-pulse photon echo (3P-PE) spectra of finite molecular systems using direct calculation from electronic Hamiltonians allows peak classification from 3P-PE spectra dynamics. Display Omitted Highlights: → RWA approach to electronic photon echo. → A straightforward calculation of 2D electronic spectrograms in finite molecular systems. → Importance of population time dynamics in relation to inter-site coherent coupling. - Abstract: The three-pulse photon echo (3P-PE) spectra of finite molecular systems and simplified line broadening models is presented. The Fourier picture of a heterodyne detected three-pulse rephasing PE signal in the δ-pulse limit of the external field is derived in analytic form. The method includes contributions of one and two-excitonic states and allows direct calculation of Fourier PE spectrogram from corresponding Hamiltonian. As an illustration, the proposed treatment is applied to simple systems, e.g. 2-site two-level system (TLS) and n-site TLS model of photosynthetic unit. The importance of relation between Fourier picture of 3P-PE dynamics (corresponding to nonzero population time, T) and coherent inter-state coupling is emphasized.

Electron and photon reconstruction and performance in ATLAS using a dynamical, topological cell clustering-based approach

CERN Document Server

The ATLAS collaboration

2017-01-01

The electron and photon reconstruction in ATLAS has moved towards the use of a dynamical, topo- logical cell-based approach for cluster building, owing to advancements in the calibration procedure which allow for such a method to be applied. The move to this new technique allows for improved measurements of electron and photon energies, particularly in situations where an electron radiates a bremsstrahlung photon, or a photon converts to an electron-poistron pair. This note details the changes to the ATLAS electron and photon reconstruction software, and assesses its performance under current LHC luminosity conditions using simulated data. Changes to the converted photon reconstruction are also detailed, which improve the reconstruction efficiency of double-track converted photons, as well as reducing the reconstruction of spurious one-track converted photons. The performance of the new reconstruction algorithm is also presented in a number of important topologies relevant to precision Standard Model physics,...

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

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-

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…

Renormalization group-theoretic approach to electron localization in disordered systems

International Nuclear Information System (INIS)

Kumar, N.; Heinrichs, J.

1977-06-01

The localization problem for the Anderson tight-binding model with site-diagonal (gaussian) disorder is studied, using a previously established analogy between this problem and the statistical mechanics of a zero-component classical field. The equivalent free-energy functional turns out to have complex coefficients in the bilinear terms but involves a real repulsive quartic interaction. The averaged one-electron propagator corresponds to the two-point correlation function for the equivalent statistical problem and the critical point gives the mobility edge, which is identified with the (real) fixed point energy of the associated renormalization group. Since for convergence reasons the conventional perturbative treatment of Wilson's formula is invalid, it is resorted to a non-perturbative approach which leads to a physical fixed point corresponding to a repulsive quartic interaction. The results for the mobility edge in three dimensions and for the critical disorder for an Anderson transition in two dimensions agree well with previous detailed predictions. The critical indices describing the approach of the transition at the mobility edge of various physical quantities, within the epsilon-expansion are also discussed. The more general problem where both diagonal and off-diagonal disorder is present in the Anderson hamiltonian is considered. In this case it is shown that the Hamilton function for the equivalent zero-component classical field model involves an additional biquadratic exchange term. From a simple generalization of Wilson's recursion relation and its non-perturbative solution explicit expressions for the mobility edges for weak diagonal and off-diagonal disorder in two and three dimensions are obtained. Our treatment casts doubts on the validity of recent conclusions about electron localization based on the renormalization group study of the nm-component spin model

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.

A Vector Approach to Regression Analysis and Its Implications to Heavy-Duty Diesel Emissions

Energy Technology Data Exchange (ETDEWEB)

McAdams, H.T.

2001-02-14

An alternative approach is presented for the regression of response data on predictor variables that are not logically or physically separable. The methodology is demonstrated by its application to a data set of heavy-duty diesel emissions. Because of the covariance of fuel properties, it is found advantageous to redefine the predictor variables as vectors, in which the original fuel properties are components, rather than as scalars each involving only a single fuel property. The fuel property vectors are defined in such a way that they are mathematically independent and statistically uncorrelated. Because the available data set does not allow definitive separation of vehicle and fuel effects, and because test fuels used in several of the studies may be unrealistically contrived to break the association of fuel variables, the data set is not considered adequate for development of a full-fledged emission model. Nevertheless, the data clearly show that only a few basic patterns of fuel-property variation affect emissions and that the number of these patterns is considerably less than the number of variables initially thought to be involved. These basic patterns, referred to as ''eigenfuels,'' may reflect blending practice in accordance with their relative weighting in specific circumstances. The methodology is believed to be widely applicable in a variety of contexts. It promises an end to the threat of collinearity and the frustration of attempting, often unrealistically, to separate variables that are inseparable.

On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

Science.gov (United States)

Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas

2011-01-01

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…

A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age

Directory of Open Access Journals (Sweden)

Marko Wilke

2018-02-01

Full Text Available This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1–75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender as well as technical (field strength, data quality predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php. Keywords: MRI template creation, Multivariate adaptive regression splines, DARTEL, Structural MRI

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

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin

2015-04-03

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

Fast metabolite identification with Input Output Kernel Regression

Science.gov (United States)

Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

2016-01-01

Motivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. Availability and implementation: Contact: celine.brouard@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307628

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

KAUST Repository

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

2012-01-01

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

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

KAUST Repository

Wei, Jiawei

2012-12-04

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

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

Science.gov (United States)

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric

Least square regression based integrated multi-parameteric demand modeling for short term load forecasting

International Nuclear Information System (INIS)

Halepoto, I.A.; Uqaili, M.A.

2014-01-01

Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintained. (author)

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

Science.gov (United States)

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

2009-11-01

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

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

Modeling geochemical datasets for source apportionment: Comparison of least square regression and inversion approaches.

Digital Repository Service at National Institute of Oceanography (India)

Tripathy, G.R.; Das, Anirban.

used methods, the Least Square Regression (LSR) and Inverse Modeling (IM), to determine the contributions of (i) solutes from different sources to global river water, and (ii) various rocks to a glacial till. The purpose of this exercise is to compare...

Lie algebraic approach to valence bond theory of π-electron systems: a preliminary study of excited states

Science.gov (United States)

Paldus, J.; Li, X.

1992-10-01

Following a brief outline of various developments and exploitations of the unitary group approach (UGA), and its extension referred to as Clifford algebra UGA (CAUGA), in molecular electronic structure calculations, we present a summary of a recently introduced implementation of CAUGA for the valence bond (VB) method based on the Pariser-Parr-Pople (PPP)-type Hamiltonian. The existing applications of this PPP-VB approach have been limited to groundstates of various π-electron systems or, at any rate, to the lowest states of a given multiplicity. In this paper the method is applied to the low-lying excited states of several archetypal models, namely cyclobutadiene and benzene, representing antiaromatic and aromatic systems, hexatriene, representing linear polyenic systems and, finally, naphthalene, representing polyacenes.

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.

Confinement effects on electron and phonon degrees of freedom in nanofilm superconductors: A Green function approach

Science.gov (United States)

Saniz, R.; Partoens, B.; Peeters, F. M.

2013-02-01

The Green function approach to the Bardeen-Cooper-Schrieffer theory of superconductivity is used to study nanofilms. We go beyond previous models and include effects of confinement on the strength of the electron-phonon coupling as well as on the electronic spectrum and on the phonon modes. Within our approach, we find that in ultrathin films, confinement effects on the electronic screening become very important. Indeed, contrary to what has been advanced in recent years, the sudden increases of the density of states when new bands start to be occupied as the film thickness increases, tend to suppress the critical temperature rather than to enhance it. On the other hand, the increase of the number of phonon modes with increasing number of monolayers in the film leads to an increase in the critical temperature. As a consequence, the superconducting critical parameters in such nanofilms are determined by these two competing effects. Furthermore, in sufficiently thin films, the condensate consists of well-defined subcondensates associated with the occupied bands, each with a distinct coherence length. The subcondensates can interfere constructively or destructively giving rise to an interference pattern in the Cooper pair probability density.

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

Paradox of spontaneous cancer regression: implications for fluctuational radiothermy and radiotherapy

International Nuclear Information System (INIS)

Roy, Prasun K.; Dutta Majumder, D.; Biswas, Jaydip

1999-01-01

Spontaneous regression of malignant tumours without treatment is a most enigmatic phenomenon with immense therapeutic potentialities. We analyse such cases to find that the commonest cause is a preceding episode of high fever-induced thermal fluctuation which produce fluctuation of biochemical and immunological parameters. Using Prigogine-Glansdorff thermodynamic stability formalism and biocybernetic principles, we develop the theoretical foundation of tumour regression induced by thermal, radiational or oxygenational fluctuations. For regression, a preliminary threshold condition of fluctuations is derived, namely σ > 2.83. We present some striking confirmation of such fluctuation-induced regression of various therapy-resistant masses as Ewing tumour, neurogranuloma and Lewis lung carcinoma by utilising σ > 2.83. Our biothermodynamic stability model of malignancy appears to illuminate the marked increase of aggressiveness of mammalian malignancy which occurred around 250 million years ago when homeothermic warm-blooded pre-mammals evolved. Using experimental data, we propose a novel approach of multi-modal hyper-fluctuation therapy involving modulation of radiotherapeutic hyper-fractionation, temperature, radiothermy and immune-status. (author)

Use of probabilistic weights to enhance linear regression myoelectric control

Science.gov (United States)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2015-12-01

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.

Science.gov (United States)

Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J

2016-04-01

The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)-an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts' law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.

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...

SUSTAINABLE MANAGEMENT APPROACHES AND REVITALIZATION TOOLS-ELECTRONIC (SMARTE): OVERVIEW AND DEMONSTRATION FOR FINAL PHASE 3 CONFERENCE

Science.gov (United States)

The U.S. contingent of the U.S.-German Bilateral Working Group is developing Sustainable Management Approaches and Revitalization Tools-electronic (SMARTe). SMARTe is a web-based, decision support system designed to assist stakeholders in developing and evaluating alternative reu...

A generalized right truncated bivariate Poisson regression model with applications to health data.

Science.gov (United States)

Islam, M Ataharul; Chowdhury, Rafiqul I

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

Optimized support vector regression for drilling rate of penetration estimation

Science.gov (United States)

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

2015-12-01

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

Model Development for MODIS Thermal Band Electronic Crosstalk

Science.gov (United States)

Chang, Tiejun; Wu, Aisheng; Geng, Xu; Li, Yonghonh; Brinkman, Jake; Keller, Graziela; Xiong, Xiaoxiong

2016-01-01

MODerate-resolution Imaging Spectroradiometer (MODIS) has 36 bands. Among them, 16 thermal emissive bands covering a wavelength range from 3.8 to 14.4 m. After 16 years on-orbit operation, the electronic crosstalk of a few Terra MODIS thermal emissive bands developed substantial issues that cause biases in the EV brightness temperature measurements and surface feature contamination. The crosstalk effects on band 27 with center wavelength at 6.7 m and band 29 at 8.5 m increased significantly in recent years, affecting downstream products such as water vapor and cloud mask. The crosstalk effect is evident in the near-monthly scheduled lunar measurements, from which the crosstalk coefficients can be derived. The development of an alternative approach is very helpful for independent verification.In this work, a physical model was developed to assess the crosstalk impact on calibration as well as in Earth view brightness temperature retrieval. This model was applied to Terra MODIS band 29 empirically to correct the Earth brightness temperature measurements. In the model development, the detectors nonlinear response is considered. The impact of the electronic crosstalk is assessed in two steps. The first step consists of determining the impact on calibration using the on-board blackbody (BB). Due to the detectors nonlinear response and large background signal, both linear and nonlinear coefficients are affected by the crosstalk from sending bands. The second step is to calculate the effects on the Earth view brightness temperature retrieval. The effects include those from affected calibration coefficients and the contamination of Earth view measurements. This model links the measurement bias with crosstalk coefficients, detector non-linearity, and the ratio of Earth measurements between the sending and receiving bands. The correction of the electronic cross talk can be implemented empirically from the processed bias at different brightness temperature. The implementation

Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data

Science.gov (United States)

Ulbrich, N.

2015-01-01

An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.

Dynamical simulation of electron transfer processes in self-assembled monolayers at metal surfaces using a density matrix approach.

Science.gov (United States)

Prucker, V; Bockstedte, M; Thoss, M; Coto, P B

2018-03-28

A single-particle density matrix approach is introduced to simulate the dynamics of heterogeneous electron transfer (ET) processes at interfaces. The characterization of the systems is based on a model Hamiltonian parametrized by electronic structure calculations and a partitioning method. The method is applied to investigate ET in a series of nitrile-substituted (poly)(p-phenylene)thiolate self-assembled monolayers adsorbed at the Au(111) surface. The results show a significant dependence of the ET on the orbital symmetry of the donor state and on the molecular and electronic structure of the spacer.

Dynamical simulation of electron transfer processes in self-assembled monolayers at metal surfaces using a density matrix approach

Science.gov (United States)

Prucker, V.; Bockstedte, M.; Thoss, M.; Coto, P. B.

2018-03-01

A single-particle density matrix approach is introduced to simulate the dynamics of heterogeneous electron transfer (ET) processes at interfaces. The characterization of the systems is based on a model Hamiltonian parametrized by electronic structure calculations and a partitioning method. The method is applied to investigate ET in a series of nitrile-substituted (poly)(p-phenylene)thiolate self-assembled monolayers adsorbed at the Au(111) surface. The results show a significant dependence of the ET on the orbital symmetry of the donor state and on the molecular and electronic structure of the spacer.

Regressive Progression: The Quest for Self-Transcendence in Western Tragedy

Directory of Open Access Journals (Sweden)

Bahee Hadaegh

2009-07-01

Full Text Available Regressive progression is a concept which interestingly describes the developmental process of Western tragedy based on the recurring motif of the quest for the higher self and Nietzsche’s understanding of Dionysian tragic hero. This motif reveals itself in three manifestations - action, imagination and inaction- respectively visible in the three major dramatic eras of the Renaissance tragedy, European nineteenth-century drama, and the Absurd Theatre. Although the approach of the quest regressively shifts from action to inaction, the degree of success of the tragic questers in approximating the wished-for higher self reveals a progressive line in the developmental process of Western tragedy.

Stochastic development regression on non-linear manifolds

DEFF Research Database (Denmark)

Kühnel, Line; Sommer, Stefan Horst

2017-01-01

We introduce a regression model for data on non-linear manifolds. The model describes the relation between a set of manifold valued observations, such as shapes of anatomical objects, and Euclidean explanatory variables. The approach is based on stochastic development of Euclidean diffusion...... processes to the manifold. Defining the data distribution as the transition distribution of the mapped stochastic process, parameters of the model, the non-linear analogue of design matrix and intercept, are found via maximum likelihood. The model is intrinsically related to the geometry encoded...

A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age.

Science.gov (United States)

Wilke, Marko

2018-02-01

This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter) from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1-75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI) were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender) as well as technical (field strength, data quality) predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php.

Demographic and socioeconomic disparity in nutrition: application of a novel Correlated Component Regression approach

Science.gov (United States)

Alkerwi, Ala'a; Vernier, Céderic; Sauvageot, Nicolas; Crichton, Georgina E; Elias, Merrill F

2015-01-01

Objectives This study aimed to examine the most important demographic and socioeconomic factors associated with diet quality, evaluated in terms of compliance with national dietary recommendations, selection of healthy and unhealthy food choices, energy density and food variety. We hypothesised that different demographic and socioeconomic factors may show disparate associations with diet quality. Study design A nationwide, cross-sectional, population-based study. Participants A total of 1352 apparently healthy and non-institutionalised subjects, aged 18–69 years, participated in the Observation of Cardiovascular Risk Factors in Luxembourg (ORISCAV-LUX) study in 2007–2008. The participants attended the nearest study centre after a telephone appointment, and were interviewed by trained research staff. Outcome measures Diet quality as measured by 5 dietary indicators, namely, recommendation compliance index (RCI), recommended foods score (RFS), non-recommended foods score (non-RFS), energy density score (EDS), and dietary diversity score (DDS). The novel Correlated Component Regression (CCR) technique was used to determine the importance and magnitude of the association of each socioeconomic factor with diet quality, in a global analytic approach. Results Increasing age, being male and living below the poverty threshold were predominant factors associated with eating a high energy density diet. Education level was an important factor associated with healthy and adequate food choices, whereas economic resources were predominant factors associated with food diversity and energy density. Conclusions Multiple demographic and socioeconomic circumstances were associated with different diet quality indicators. Efforts to improve diet quality for high-risk groups need an important public health focus. PMID:25967988

Real-space grids and the Octopus code as tools for the development of new simulation approaches for electronic systems

Science.gov (United States)

Andrade, Xavier; Strubbe, David; De Giovannini, Umberto; Larsen, Ask Hjorth; Oliveira, Micael J. T.; Alberdi-Rodriguez, Joseba; Varas, Alejandro; Theophilou, Iris; Helbig, Nicole; Verstraete, Matthieu J.; Stella, Lorenzo; Nogueira, Fernando; Aspuru-Guzik, Alán; Castro, Alberto; Marques, Miguel A. L.; Rubio, Angel

Real-space grids are a powerful alternative for the simulation of electronic systems. One of the main advantages of the approach is the flexibility and simplicity of working directly in real space where the different fields are discretized on a grid, combined with competitive numerical performance and great potential for parallelization. These properties constitute a great advantage at the time of implementing and testing new physical models. Based on our experience with the Octopus code, in this article we discuss how the real-space approach has allowed for the recent development of new ideas for the simulation of electronic systems. Among these applications are approaches to calculate response properties, modeling of photoemission, optimal control of quantum systems, simulation of plasmonic systems, and the exact solution of the Schr\\"odinger equation for low-dimensionality systems.

Dimer and cluster approach for the evaluation of electronic couplings governing charge transport: Application to two pentacene polymorphs

International Nuclear Information System (INIS)

Canola, Sofia; Pecoraro, Claudia; Negri, Fabrizia

2016-01-01

Hole transport properties are modeled for two polymorphs of pentacene: the single crystal polymorph and the thin film polymorph relevant for organic thin-film transistor applications. Electronic couplings are evaluated in the standard dimer approach but also considering a cluster approach in which the central molecule is surrounded by a large number of molecules quantum-chemically described. The effective electronic couplings suitable for the parametrization of a tight-binding model are derived either from the orthogonalization scheme limited to HOMO orbitals and from the orthogonalization of the full basis of molecular orbitals. The angular dependent mobilities estimated for the two polymorphs using the predicted pattern of couplings display different anisotropy characteristics as suggested from experimental investigations.

Dimer and cluster approach for the evaluation of electronic couplings governing charge transport: Application to two pentacene polymorphs

Energy Technology Data Exchange (ETDEWEB)

Canola, Sofia; Pecoraro, Claudia; Negri, Fabrizia

2016-10-20

Hole transport properties are modeled for two polymorphs of pentacene: the single crystal polymorph and the thin film polymorph relevant for organic thin-film transistor applications. Electronic couplings are evaluated in the standard dimer approach but also considering a cluster approach in which the central molecule is surrounded by a large number of molecules quantum-chemically described. The effective electronic couplings suitable for the parametrization of a tight-binding model are derived either from the orthogonalization scheme limited to HOMO orbitals and from the orthogonalization of the full basis of molecular orbitals. The angular dependent mobilities estimated for the two polymorphs using the predicted pattern of couplings display different anisotropy characteristics as suggested from experimental investigations.

Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks

Science.gov (United States)

Gray-Davies, Tristan; Holmes, Chris C.; Caron, François

2018-01-01

We present a novel Bayesian nonparametric regression model for covariates X and continuous response variable Y ∈ ℝ. The model is parametrized in terms of marginal distributions for Y and X and a regression function which tunes the stochastic ordering of the conditional distributions F (y|x). By adopting an approximate composite likelihood approach, we show that the resulting posterior inference can be decoupled for the separate components of the model. This procedure can scale to very large datasets and allows for the use of standard, existing, software from Bayesian nonparametric density estimation and Plackett-Luce ranking estimation to be applied. As an illustration, we show an application of our approach to a US Census dataset, with over 1,300,000 data points and more than 100 covariates. PMID:29623150

Paper electronics.

Science.gov (United States)

Tobjörk, Daniel; Österbacka, Ronald

2011-05-03

Paper is ubiquitous in everyday life and a truly low-cost substrate. The use of paper substrates could be extended even further, if electronic applications would be applied next to or below the printed graphics. However, applying electronics on paper is challenging. The paper surface is not only very rough compared to plastics, but is also porous. While this is detrimental for most electronic devices manufactured directly onto paper substrates, there are also approaches that are compatible with the rough and absorptive paper surface. In this review, recent advances and possibilities of these approaches are evaluated and the limitations of paper electronics are discussed. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Asymmetrical Responses of Ecosystem Processes to Positive Versus Negative Precipitation Extremes: a Replicated Regression Experimental Approach

Science.gov (United States)

Felton, A. J.; Smith, M. D.

2016-12-01

Heightened climatic variability due to atmospheric warming is forecast to increase the frequency and severity of climate extremes. In particular, changes to interannual variability in precipitation, characterized by increases in extreme wet and dry years, are likely to impact virtually all terrestrial ecosystem processes. However, to date experimental approaches have yet to explicitly test how ecosystem processes respond to multiple levels of climatic extremity, limiting our understanding of how ecosystems will respond to forecast increases in the magnitude of climate extremes. Here we report the results of a replicated regression experimental approach, in which we imposed 9 and 11 levels of growing season precipitation amount and extremity in mesic grassland during 2015 and 2016, respectively. Each level corresponded to a specific percentile of the long-term record, which produced a large gradient of soil moisture conditions that ranged from extreme wet to extreme dry. In both 2015 and 2016, asymptotic responses to water availability were observed for soil respiration. This asymmetry was driven in part by transitions between soil moisture versus temperature constraints on respiration as conditions became increasingly dry versus increasingly wet. In 2015, aboveground net primary production (ANPP) exhibited asymmetric responses to precipitation that largely mirrored those of soil respiration. In total, our results suggest that in this mesic ecosystem, these two carbon cycle processes were more sensitive to extreme drought than to extreme wet years. Future work will assess ANPP responses for 2016, soil nutrient supply and physiological responses of the dominant plant species. Future efforts are needed to compare our findings across a diverse array of ecosystem types, and in particular how the timing and magnitude of precipitation events may modify the response of ecosystem processes to increasing magnitudes of precipitation extremes.

Analytical approach to phonons and electron-phonon interactions in single-walled zigzag carbon nanotubes

Energy Technology Data Exchange (ETDEWEB)

Kandemir, B S; Keskin, M [Department of Physics, Faculty of Sciences, Ankara University, 06100 Tandogan, Ankara (Turkey)

2008-08-13

In this paper, exact analytical expressions for the entire phonon spectra in single-walled carbon nanotubes with zigzag geometry are presented by using a new approach, originally developed by Kandemir and Altanhan. This approach is based on the concept of construction of a classical lattice Hamiltonian of single-walled carbon nanotubes, wherein the nearest and next nearest neighbor and bond bending interactions are all included, then its quantization and finally diagonalization of the resulting second quantized Hamiltonian. Furthermore, within this context, explicit analytical expressions for the relevant electron-phonon interaction coefficients are also investigated for single-walled carbon nanotubes having this geometry, by the phonon modulation of the hopping interaction.

Analytical approach to phonons and electron-phonon interactions in single-walled zigzag carbon nanotubes

International Nuclear Information System (INIS)

Kandemir, B S; Keskin, M

2008-01-01

In this paper, exact analytical expressions for the entire phonon spectra in single-walled carbon nanotubes with zigzag geometry are presented by using a new approach, originally developed by Kandemir and Altanhan. This approach is based on the concept of construction of a classical lattice Hamiltonian of single-walled carbon nanotubes, wherein the nearest and next nearest neighbor and bond bending interactions are all included, then its quantization and finally diagonalization of the resulting second quantized Hamiltonian. Furthermore, within this context, explicit analytical expressions for the relevant electron-phonon interaction coefficients are also investigated for single-walled carbon nanotubes having this geometry, by the phonon modulation of the hopping interaction

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.

Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

Science.gov (United States)

Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul

2018-05-01

The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.

Advances in the MQDT approach of electron/molecular cation reactive collisions: High precision extensive calculations for applications

Directory of Open Access Journals (Sweden)

Motapon O.

2015-01-01

Full Text Available Recent advances in the stepwise multichannel quantum defect theory approach of electron/molecular cation reactive collisions have been applied to perform computations of cross sections and rate coefficients for dissociative recombination and electron-impact ro-vibrational transitions of H2+, BeH+ and their deuterated isotopomers. At very low energy, rovibronic interactions play a significant role in the dynamics, whereas at high energy, the dissociative excitation strongly competes with all other reactive processes.

Phase-space description of wave packet approach to electronic transport in nanoscale systems

International Nuclear Information System (INIS)

Szydłowski, D; Wołoszyn, M; Spisak, B J

2013-01-01

The dynamics of conduction electrons in resonant tunnelling nanosystems is studied within the phase-space approach based on the Wigner distribution function. The time evolution of the distribution function is calculated from the time-dependent quantum kinetic equation for which an effective numerical method is presented. Calculations of the transport properties of a double-barrier resonant tunnelling diode are performed to illustrate the proposed techniques. Additionally, analysis of the transient effects in the nanosystem is carried out and it is shown that for some range of the bias voltage the temporal variations of electronic current can take negative values. The explanation of this effect is based on the analysis of the time changes of the Wigner distribution function. The decay time of the temporal current oscillations in the nanosystem as a function of the bias voltage is determined. (paper)

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

Science.gov (United States)

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

2013-01-01

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

Adaptive kernel regression for freehand 3D ultrasound reconstruction

Science.gov (United States)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements

KAUST Repository

Ryu, Duchwan; Li, Erning; Mallick, Bani K.

2010-01-01

" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.

Regression testing in the TOTEM DCS

International Nuclear Information System (INIS)

Rodríguez, F Lucas; Atanassov, I; Burkimsher, P; Frost, O; Taskinen, J; Tulimaki, V

2012-01-01

The Detector Control System of the TOTEM experiment at the LHC is built with the industrial product WinCC OA (PVSS). The TOTEM system is generated automatically through scripts using as input the detector Product Breakdown Structure (PBS) structure and its pinout connectivity, archiving and alarm metainformation, and some other heuristics based on the naming conventions. When those initial parameters and automation code are modified to include new features, the resulting PVSS system can also introduce side-effects. On a daily basis, a custom developed regression testing tool takes the most recent code from a Subversion (SVN) repository and builds a new control system from scratch. This system is exported in plain text format using the PVSS export tool, and compared with a system previously validated by a human. A report is sent to the developers with any differences highlighted, in readiness for validation and acceptance as a new stable version. This regression approach is not dependent on any development framework or methodology. This process has been satisfactory during several months, proving to be a very valuable tool before deploying new versions in the production systems.

Regional trends in short-duration precipitation extremes: a flexible multivariate monotone quantile regression approach

Science.gov (United States)

Cannon, Alex

2017-04-01

Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a

Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression

NARCIS (Netherlands)

D.E. Allen (David); A.K. Singh (Abhay); R.J. Powell (Robert); M.J. McAleer (Michael); J. Taylor (James); L. Thomas (Lyn)

2013-01-01

textabstractThe purpose of this paper is to examine the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using linear and non linear quantile regression approach. Our goal in this paper is to demonstrate that the relationship between the

Practical Approaches to Mitigation of Specimen Charging in High-Resolution Transmission Electron Microscopy

Directory of Open Access Journals (Sweden)

Young-Min Kim

2010-09-01

Full Text Available Specimen charging that is associated with the electron bombardment on the sample is a practical hindrance to high-resolution transmission electron microscopy (HRTEM analysis because it causes a severe loss of resolution in either diffraction or image data. Conductive thin film deposition on an insulating specimen has been proposed as an effective approach to the mitigation of the specimen charging; however, this method is generally not useful in HRTEM imaging of materials because the deposited film induces another artifact in the HRTEM image contrast. In this study, we propose practical methods to mitigate the specimen charging that takes place during the HRTEM of materials. For bulk-type specimens prepared by either an ion-thinning or focused-ion beam (FIB process, a plasma cleaning treatment is significantly effective in eliminating the charging phenomenon. In the case of low-dimensional nanomaterials such as nanowires and nanoparticles, the plasma cleaning is not feasible; however, the charging effect can be effectively eliminated by adjusting the electron illumination condition. The proposed methods facilitate a decrease in the buildup of specimen charging, thereby enhancing the quality of high-resolution images significantly.

Electron-positron correlations in an electron liquid

International Nuclear Information System (INIS)

Stachowiak, H.

1980-01-01

The importance of studying electron-positron interaction for the interpretation of angular correlation data obtained for metallic systems is emphasized. The most successful approaches to electron-positron correlations in jellium are presented. Those include the Bethe-Goldstone two-body equation proposed by Kahana, the charge-density-dielectric function approach connected with the names of Singwi, Sjolander, Stott and Bhattacharyya and the Sawada boson-generalized Tamm-Dancoff approach elaborated recently by Arponen and Pajanne. In conclusion, it is reported that one can consider that the behaviour of a positron at rest in jellium is relatively well understood, though the problem of the optimal choice of a two-body electron-positron phenomenological equation is still open. Also, the behaviour of a positron in a real metal is not well understood and so far, serious calculations in this field have been performed only on very simple models while realistic calculations of the ACPAQ curves tend to minimize the importance of the problems which remain to be solved. (K.B.)

Linear-algebraic approach to electronic excitation of atoms and molecules by electron impact

International Nuclear Information System (INIS)

Collins, L.A.; Schneider, B.I.

1983-01-01

A linear-algebraic method, based on an integral equations formulation, is applied to the excitation of atoms and molecules by electron impact. Various schemes are devised for treating the one-electron terms that sometimes cause instabilities when directly incorporated into the solution matrix. These include introducing Lagrange undetermined multipliers and correlation terms. Good agreement between the method and other computational techniques is obtained for electron scattering for hydrogenic and Li-like atomic ions and for H 2 + in two- to five-state close-coupling calculations

Fixed kernel regression for voltammogram feature extraction

International Nuclear Information System (INIS)

Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N

2009-01-01

Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals

Inverse Problem Approach for the Alignment of Electron Tomographic Series

International Nuclear Information System (INIS)

Tran, V.D.; Moreaud, M.; Thiebaut, E.; Denis, L.; Becker, J.M.

2014-01-01

In the refining industry, morphological measurements of particles have become an essential part in the characterization catalyst supports. Through these parameters, one can infer the specific physico-chemical properties of the studied materials. One of the main acquisition techniques is electron tomography (or nano-tomography). 3D volumes are reconstructed from sets of projections from different angles made by a Transmission Electron Microscope (TEM). This technique provides a real three-dimensional information at the nano-metric scale. A major issue in this method is the misalignment of the projections that contributes to the reconstruction. The current alignment techniques usually employ fiducial markers such as gold particles for a correct alignment of the images. When the use of markers is not possible, the correlation between adjacent projections is used to align them. However, this method sometimes fails. In this paper, we propose a new method based on the inverse problem approach where a certain criterion is minimized using a variant of the Nelder and Mead simplex algorithm. The proposed approach is composed of two steps. The first step consists of an initial alignment process, which relies on the minimization of a cost function based on robust statistics measuring the similarity of a projection to its previous projections in the series. It reduces strong shifts resulting from the acquisition between successive projections. In the second step, the pre-registered projections are used to initialize an iterative alignment-refinement process which alternates between (i) volume reconstructions and (ii) registrations of measured projections onto simulated projections computed from the volume reconstructed in (i). At the end of this process, we have a correct reconstruction of the volume, the projections being correctly aligned. Our method is tested on simulated data and shown to estimate accurately the translation, rotation and scale of arbitrary transforms. We

Estimation of Production KWS Maize Hybrids Using Nonlinear Regression

Directory of Open Access Journals (Sweden)

Florica MORAR

2018-06-01

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

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR is an efficient tool for metamodelling of nonlinear dynamic models

Directory of Open Access Journals (Sweden)

Omholt Stig W

2011-06-01

Full Text Available Abstract Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs to variation in features of the trajectories of the state variables (outputs throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR, where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR and ordinary least squares (OLS regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

Science.gov (United States)

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for

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)

Multilevel covariance regression with correlated random effects in the mean and variance structure.

Science.gov (United States)

Quintero, Adrian; Lesaffre, Emmanuel

2017-09-01

Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry.

Science.gov (United States)

Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H

2014-12-30

For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. Copyright © 2014 John Wiley & Sons

Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach.

Science.gov (United States)

Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien

2017-07-24

Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Time series regression-based pairs trading in the Korean equities market

Science.gov (United States)

Kim, Saejoon; Heo, Jun

2017-07-01

Pairs trading is an instance of statistical arbitrage that relies on heavy quantitative data analysis to profit by capitalising low-risk trading opportunities provided by anomalies of related assets. A key element in pairs trading is the rule by which open and close trading triggers are defined. This paper investigates the use of time series regression to define the rule which has previously been identified with fixed threshold-based approaches. Empirical results indicate that our approach may yield significantly increased excess returns compared to ones obtained by previous approaches on large capitalisation stocks in the Korean equities market.

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)

Penalized regression procedures for variable selection in the potential outcomes framework.

Science.gov (United States)

Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L

2015-05-10

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple 'impute, then select' class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data, and imputation are drawn. A difference least absolute shrinkage and selection operator algorithm is defined, along with its multiple imputation analogs. The procedures are illustrated using a well-known right-heart catheterization dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Modeling a terminology-based electronic nursing record system: an object-oriented approach.

Science.gov (United States)

Park, Hyeoun-Ae; Cho, InSook; Byeun, NamSoo

2007-10-01

The aim of this study was to present our perspectives on healthcare information analysis at a conceptual level and the lessons learned from our experience with the development of a terminology-based enterprise electronic nursing record system - which was one of components in an EMR system at a tertiary teaching hospital in Korea - using an object-oriented system analysis and design concept. To ensure a systematic approach and effective collaboration, the department of nursing constituted a system modeling team comprising a project manager, systems analysts, user representatives, an object-oriented methodology expert, and healthcare informaticists (including the authors). A rational unified process (RUP) and the Unified Modeling Language were used as a development process and for modeling notation, respectively. From the scenario and RUP approach, user requirements were formulated into use case sets and the sequence of activities in the scenario was depicted in an activity diagram. The structure of the system was presented in a class diagram. This approach allowed us to identify clearly the structural and behavioral states and important factors of a terminology-based ENR system (e.g., business concerns and system design concerns) according to the viewpoints of both domain and technical experts.

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.

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

Science.gov (United States)

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

2016-10-01

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

Representing electrons a biographical approach to theoretical entities

CERN Document Server

Arabatzis, Theodore

2006-01-01

Both a history and a metahistory, Representing Electrons focuses on the development of various theoretical representations of electrons from the late 1890s to 1925 and the methodological problems associated with writing about unobservable scientific entities. Using the electron-or rather its representation-as a historical actor, Theodore Arabatzis illustrates the emergence and gradual consolidation of its representation in physics, its career throughout old quantum theory, and its appropriation and reinterpretation by chemists. As Arabatzis develops this novel biographical

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....

A regression modeling approach for studying carbonate system variability in the northern Gulf of Alaska

Science.gov (United States)

Evans, Wiley; Mathis, Jeremy T.; Winsor, Peter; Statscewich, Hank; Whitledge, Terry E.

2013-01-01

northern Gulf of Alaska (GOA) shelf experiences carbonate system variability on seasonal and annual time scales, but little information exists to resolve higher frequency variability in this region. To resolve this variability using platforms-of-opportunity, we present multiple linear regression (MLR) models constructed from hydrographic data collected along the Northeast Pacific Global Ocean Ecosystems Dynamics (GLOBEC) Seward Line. The empirical algorithms predict dissolved inorganic carbon (DIC) and total alkalinity (TA) using observations of nitrate (NO3-), temperature, salinity and pressure from the surface to 500 m, with R2s > 0.97 and RMSE values of 11 µmol kg-1 for DIC and 9 µmol kg-1 for TA. We applied these relationships to high-resolution NO3- data sets collected during a novel 20 h glider flight and a GLOBEC mesoscale SeaSoar survey. Results from the glider flight demonstrated time/space along-isopycnal variability of aragonite saturations (Ωarag) associated with a dicothermal layer (a cold near-surface layer found in high latitude oceans) that rivaled changes seen vertically through the thermocline. The SeaSoar survey captured the uplift to aragonite saturation horizon (depth where Ωarag = 1) shoaled to a previously unseen depth in the northern GOA. This work is similar to recent studies aimed at predicting the carbonate system in continental margin settings, albeit demonstrates that a NO3--based approach can be applied to high-latitude data collected from platforms capable of high-frequency measurements.

A new neutron interferometry approach in the determination of the neutron-electron interaction amplitude

CERN Document Server

Ioffe, A

2002-01-01

A new experimental approach in the determination of the neutron-electron interaction amplitude is proposed. The main idea of this approach is to use a perfect-crystal neutron interferometer as both a sample and a device for the measurement of the extra phase shift caused by the neutron interaction with atoms of Si. Indeed, such a sample (an interferometer blade) has a well-known atomic density and is a priori perfectly aligned with respect to the crystal lattice of the interferometer crystal. This results in the minimization of systematic errors caused by sample alignment and increases the overall experimental accuracy. Some theoretic estimations and details of an experimental setup are discussed. (orig.)

Electronic Structure Approach to Tunable Electronic Properties of Hybrid Organic-Inorganic Perovskites

Science.gov (United States)

Liu, Garnett; Huhn, William; Mitzi, David B.; Kanai, Yosuke; Blum, Volker

We present a study of the electronic structure of layered hybrid organic-inorganic perovskite (HOIP) materials using all-electron density-functional theory. Varying the nature of the organic and inorganic layers should enable systematically fine-tuning the carrier properties of each component. Using the HSE06 hybrid density functional including spin-orbit coupling (SOC), we validate the principle of tuning subsystem-specific parts of the electron band structures and densities of states in CH3NH3PbX3 (X=Cl, Br, I) compared to a modified organic component in layered (C6H5C2H4NH3) 2PbX4 (X=Cl, Br, I) and C20H22S4N2PbX4 (X=Cl, Br, I). We show that tunable shifts of electronic levels indeed arise by varying Cl, Br, I as the inorganic components, and CH3NH3+ , C6H5C2H4NH3+ , C20H22S4N22 + as the organic components. SOC is found to play an important role in splitting the conduction bands of the HOIP compounds investigated here. The frontier orbitals of the halide shift, increasing the gap, when Cl is substituted for Br and I.

New real space correlated-basis-functions approach for the electron correlations of the semiconductor inversion layer

International Nuclear Information System (INIS)

Feng Weiguo; Wang Hongwei; Wu Xiang

1989-12-01

Based on the real space Correlated-Basis-Functions theory and the collective oscillation behaviour of the electron gas with effective Coulomb interaction, the many body wave function is obtained for the quasi-two-dimensional electron system in the semiconductor inversion layer. The pair-correlation function and the correlation energy of the system have been calculated by the integro-differential method in this paper. The comparison with the other previous theoretical results is also made. The new theoretical approach and its numerical results show that the pair-correlation functions are definitely positive and satisfy the normalization condition. (author). 10 refs, 2 figs

Statistical Downscaling Output GCM Modeling with Continuum Regression and Pre-Processing PCA Approach

Directory of Open Access Journals (Sweden)

Sutikno Sutikno

2010-08-01

Full Text Available One of the climate models used to predict the climatic conditions is Global Circulation Models (GCM. GCM is a computer-based model that consists of different equations. It uses numerical and deterministic equation which follows the physics rules. GCM is a main tool to predict climate and weather, also it uses as primary information source to review the climate change effect. Statistical Downscaling (SD technique is used to bridge the large-scale GCM with a small scale (the study area. GCM data is spatial and temporal data most likely to occur where the spatial correlation between different data on the grid in a single domain. Multicollinearity problems require the need for pre-processing of variable data X. Continuum Regression (CR and pre-processing with Principal Component Analysis (PCA methods is an alternative to SD modelling. CR is one method which was developed by Stone and Brooks (1990. This method is a generalization from Ordinary Least Square (OLS, Principal Component Regression (PCR and Partial Least Square method (PLS methods, used to overcome multicollinearity problems. Data processing for the station in Ambon, Pontianak, Losarang, Indramayu and Yuntinyuat show that the RMSEP values and R2 predict in the domain 8x8 and 12x12 by uses CR method produces results better than by PCR and PLS.

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.

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-25

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.

Experimentally testing the dependence of momentum transport on second derivatives using Gaussian process regression

Science.gov (United States)

Chilenski, M. A.; Greenwald, M. J.; Hubbard, A. E.; Hughes, J. W.; Lee, J. P.; Marzouk, Y. M.; Rice, J. E.; White, A. E.

2017-12-01

It remains an open question to explain the dramatic change in intrinsic rotation induced by slight changes in electron density (White et al 2013 Phys. Plasmas 20 056106). One proposed explanation is that momentum transport is sensitive to the second derivatives of the temperature and density profiles (Lee et al 2015 Plasma Phys. Control. Fusion 57 125006), but it is widely considered to be impossible to measure these higher derivatives. In this paper, we show that it is possible to estimate second derivatives of electron density and temperature using a nonparametric regression technique known as Gaussian process regression. This technique avoids over-constraining the fit by not assuming an explicit functional form for the fitted curve. The uncertainties, obtained rigorously using Markov chain Monte Carlo sampling, are small enough that it is reasonable to explore hypotheses which depend on second derivatives. It is found that the differences in the second derivatives of n{e} and T{e} between the peaked and hollow rotation cases are rather small, suggesting that changes in the second derivatives are not likely to explain the experimental results.

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...

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

Science.gov (United States)

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Does the Magnitude of the Link between Unemployment and Crime Depend on the Crime Level? A Quantile Regression Approach

Directory of Open Access Journals (Sweden)

Horst Entorf

2015-07-01

Full Text Available Two alternative hypotheses – referred to as opportunity- and stigma-based behavior – suggest that the magnitude of the link between unemployment and crime also depends on preexisting local crime levels. In order to analyze conjectured nonlinearities between both variables, we use quantile regressions applied to German district panel data. While both conventional OLS and quantile regressions confirm the positive link between unemployment and crime for property crimes, results for assault differ with respect to the method of estimation. Whereas conventional mean regressions do not show any significant effect (which would confirm the usual result found for violent crimes in the literature, quantile regression reveals that size and importance of the relationship are conditional on the crime rate. The partial effect is significantly positive for moderately low and median quantiles of local assault rates.

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.

Regression models for categorical, count, and related variables an applied approach

CERN Document Server

Hoffmann, John P

2016-01-01

Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner.   This book provides an introduction and overview of several statistical models designed for these types of outcomes--all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis.   Numerous examples from the social sciences demonstrate the practical applications of these models. The chapte...

Casemix funding for a specialist paediatrics hospital: a hedonic regression approach.

Science.gov (United States)

Bridges, J F; Hanson, R M

2000-01-01

This paper inquires into the effects that Diagnosis Related Groups (DRGs) have had on the ability to explain patient-level costs in a specialist paediatrics hospital. Two hedonic models are estimated using 1996/97 New Children's Hospital (NCH) patient level cost data, one with and one without a casemix index (CMI). The results show that the inclusion of a casemix index as an explanatory variable leads to a better accounting of cost. The full hedonic model is then used to simulate a funding model for the 1997/98 NCH cost data. These costs are highly correlated with the actual costs reported for that year. In addition, univariate regression indicates that there has been inflation in costs in the order of 4.8% between the two years. In conclusion, hedonic analysis can provide valuable evidence for the design of funding models that account for casemix.

A statistical approach for predicting thermal diffusivity profiles in fusion plasmas as a transport model

International Nuclear Information System (INIS)

Yokoyama, Masayuki

2014-01-01

A statistical approach is proposed to predict thermal diffusivity profiles as a transport “model” in fusion plasmas. It can provide regression expressions for the ion and electron heat diffusivities (χ i and χ e ), separately, to construct their radial profiles. An approach that this letter is proposing outstrips the conventional scaling laws for the global confinement time (τ E ) since it also deals with profiles (temperature, density, heating depositions etc.). This approach has become possible with the analysis database accumulated by the extensive application of the integrated transport analysis suite to experiment data. In this letter, TASK3D-a analysis database for high-ion-temperature (high-T i ) plasmas in the LHD (Large Helical Device) is used as an example to describe an approach. (author)

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.

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.

Digital contract approach for consistent and predictable multimedia information delivery in electronic commerce

Science.gov (United States)

Konana, Prabhudev; Gupta, Alok; Whinston, Andrew B.

1997-01-01

A pure 'technological' solution to network quality problems is incomplete since any benefits from new technologies are offset by the demand from exponentially growing electronic commerce ad data-intensive applications. SInce an economic paradigm is implicit in electronic commerce, we propose a 'market-system' approach to improve quality of service. Quality of service for digital products takes on a different meaning since users view quality of service differently and value information differently. We propose a framework for electronic commerce that is based on an economic paradigm and mass-customization, and works as a wide-area distributed management system. In our framework, surrogate-servers act as intermediaries between information provides and end- users, and arrange for consistent and predictable information delivery through 'digital contracts.' These contracts are negotiated and priced based on economic principles. Surrogate servers pre-fetched, through replication, information from many different servers and consolidate based on demand expectations. In order to recognize users' requirements and process requests accordingly, real-time databases are central to our framework. We also propose that multimedia information be separated into slowly changing and rapidly changing data streams to improve response time requirements. Surrogate- servers perform the tasks of integration of these data streams that is transparent to end-users.

Deducing Electronic Unit Internal Response During a Vibration Test Using a Lumped Parameter Modeling Approach

Science.gov (United States)

Van Dyke, Michael B.

2014-01-01

During random vibration testing of electronic boxes there is often a desire to know the dynamic response of certain internal printed wiring boards (PWBs) for the purpose of monitoring the response of sensitive hardware or for post-test forensic analysis in support of anomaly investigation. Due to restrictions on internally mounted accelerometers for most flight hardware there is usually no means to empirically observe the internal dynamics of the unit, so one must resort to crude and highly uncertain approximations. One common practice is to apply Miles Equation, which does not account for the coupled response of the board in the chassis, resulting in significant over- or under-prediction. This paper explores the application of simple multiple-degree-of-freedom lumped parameter modeling to predict the coupled random vibration response of the PWBs in their fundamental modes of vibration. A simple tool using this approach could be used during or following a random vibration test to interpret vibration test data from a single external chassis measurement to deduce internal board dynamics by means of a rapid correlation analysis. Such a tool might also be useful in early design stages as a supplemental analysis to a more detailed finite element analysis to quickly prototype and analyze the dynamics of various design iterations. After developing the theoretical basis, a lumped parameter modeling approach is applied to an electronic unit for which both external and internal test vibration response measurements are available for direct comparison. Reasonable correlation of the results demonstrates the potential viability of such an approach. Further development of the preliminary approach presented in this paper will involve correlation with detailed finite element models and additional relevant test data.

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

Directory of Open Access Journals (Sweden)

Gascón Adrià

2017-10-01

Full Text Available We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013, and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

Comparing Kriging and Regression Approaches for Mapping Soil Clay Content in a diverse Danish Landscape

DEFF Research Database (Denmark)

Adhikari, Kabindra; Bou Kheir, Rania; Greve, Mette Balslev

2013-01-01

Information on the spatial variability of soil texture including soil clay content in a landscape is very important for agricultural and environmental use. Different prediction techniques are available to assess and map spatial variability of soil properties, but selecting the most suitable techn...... the prediction in OKst compared with that in OK, whereas RT showed the lowest performance of all (R2 = 0.52; RMSE = 0.52; and RPD = 1.17). We found RKrr to be an effective prediction method and recommend this method for any future soil mapping activities in Denmark....... technique at a given site has always been a major issue in all soil mapping applications. We studied the prediction performance of ordinary kriging (OK), stratified OK (OKst), regression trees (RT), and rule-based regression kriging (RKrr) for digital mapping of soil clay content at 30.4-m grid size using 6...

Quantum inelastic electron-vibration scattering in molecular wires: Landauer-like versus Green's function approaches and temperature effects

International Nuclear Information System (INIS)

Ness, H

2006-01-01

In this paper, we consider the problem of inelastic electron transport in molecular systems in which both electronic and vibrational degrees of freedom are considered on the quantum level. The electronic transport properties of the corresponding molecular nanojunctions are obtained by means of a non-perturbative Landauer-like multi-channel inelastic scattering technique. The connections between this approach and other Green's function techniques that are useful in particular cases are studied in detail. The validity of the wide-band approximation, the effects of the lead self-energy and the dynamical polaron shift are also studied for a wide range of parameters. As a practical application of the method, we consider the effects of the temperature on the conductance properties of molecular breakjunctions in relation to recent experiments

Identifying individual changes in performance with composite quality indicators while accounting for regression to the mean.

Science.gov (United States)

Gajewski, Byron J; Dunton, Nancy

2013-04-01

Almost a decade ago Morton and Torgerson indicated that perceived medical benefits could be due to "regression to the mean." Despite this caution, the regression to the mean "effects on the identification of changes in institutional performance do not seem to have been considered previously in any depth" (Jones and Spiegelhalter). As a response, Jones and Spiegelhalter provide a methodology to adjust for regression to the mean when modeling recent changes in institutional performance for one-variable quality indicators. Therefore, in our view, Jones and Spiegelhalter provide a breakthrough methodology for performance measures. At the same time, in the interests of parsimony, it is useful to aggregate individual quality indicators into a composite score. Our question is, can we develop and demonstrate a methodology that extends the "regression to the mean" literature to allow for composite quality indicators? Using a latent variable modeling approach, we extend the methodology to the composite indicator case. We demonstrate the approach on 4 indicators collected by the National Database of Nursing Quality Indicators. A simulation study further demonstrates its "proof of concept."

Robust Mediation Analysis Based on Median Regression

Science.gov (United States)

Yuan, Ying; MacKinnon, David P.

2014-01-01

Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925

Evaluating Electronic Reference Services: Issues, Approaches and Criteria.

Science.gov (United States)

Novotny, Eric

2001-01-01

Discussion of electronic library reference services focuses on an overview of the chief methodologies available for conducting assessments of electronic services. Highlights include quantitative measures and benchmarks, including equity and access; quality measures; behavioral aspects of quality, including librarian-patron interaction; and future…

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.

Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution

Directory of Open Access Journals (Sweden)

Petrovic Nemanja

2007-01-01

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

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).

Manipulating the electron distribution through a combination of electron injection and MacKenzie’s Maxwell Demon

International Nuclear Information System (INIS)

Yip, Chi-Shung; Hershkowitz, Noah

2015-01-01

Experiments on electron heating are performed in a biased hot filament-produced argon plasma. Electrons are confined by multi-dipole magnetic fields on the radial wall of the cylindrical chamber but not the planar end walls. Electron heating is provided by a combination of cold electron injection (Hershowitz N and Leung K N 1975 Appl. Phys. Lett. 26 607) and a MacKenzie Maxwell Demon (Mackenzie K R et al 1971 Appl. Phys. Lett. 18 529). This approach allows the manipulation of the electrons by introducing a depleted tail into the electron energy distribution function or by removing a depleted tail. It is found that the injected electrons mimic and thermalize with the electron species with the closest average energy or temperature. The effect of the injected electrons is optimal when they mimic the secondary electrons emitted from the wall instead of the degraded primary electrons. Both approaches combine to achieve increases in electron temperature T e from 0.67 to 2.8 eV, which was not significantly higher than using each approach alone. (paper)

Electronic Health Record-Enabled Big-Data Approaches to Nephrotoxin-Associated Acute Kidney Injury Risk Prediction.

Science.gov (United States)

Sutherland, Scott M

2018-06-09

Nephrotoxin-associated acute kidney injury (NTx-AKI) has become one of the most common causes of AKI among hospitalized adults and children; across acute and intensive care populations, exposure to nephrotoxins accounts for 15-25% of AKI. Although some interventions have shown promise in observational studies, no treatments currently exist for NTx-AKI once it occurs. Thus, nearly all effective strategies are aimed at prevention. The primary obstacle to prevention is risk prediction and the determination of which patients are more likely to develop NTx-AKI when exposed to medications with nephrotoxic potential. Historically, traditional statistical modeling has been applied to previously recognized clinical risk factors to identify predictors of NTx-AKI. However, increased electronic health record adoption and the evolution of "big-data" approaches to predictive analytics may offer a unique opportunity to prevent NTx-AKI events. This article describes prior and current approaches to NTx-AKI prediction and offers three novel use cases for electronic health record-enabled NTx-AKI forecasting and risk profiling. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Accounting for measurement error in log regression models with applications to accelerated testing.

Science.gov (United States)

Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

2018-01-01

In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

Accounting for measurement error in log regression models with applications to accelerated testing.

Directory of Open Access Journals (Sweden)

Robert Richardson

Full Text Available In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach

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Baydaroğlu, Özlem; Koçak, Kasım; Duran, Kemal

2018-06-01

Prediction of water amount that will enter the reservoirs in the following month is of vital importance especially for semi-arid countries like Turkey. Climate projections emphasize that water scarcity will be one of the serious problems in the future. This study presents a methodology for predicting river flow for the subsequent month based on the time series of observed monthly river flow with hybrid models of support vector regression (SVR). Monthly river flow over the period 1940-2012 observed for the Kızılırmak River in Turkey has been used for training the method, which then has been applied for predictions over a period of 3 years. SVR is a specific implementation of support vector machines (SVMs), which transforms the observed input data time series into a high-dimensional feature space (input matrix) by way of a kernel function and performs a linear regression in this space. SVR requires a special input matrix. The input matrix was produced by wavelet transforms (WT), singular spectrum analysis (SSA), and a chaotic approach (CA) applied to the input time series. WT convolutes the original time series into a series of wavelets, and SSA decomposes the time series into a trend, an oscillatory and a noise component by singular value decomposition. CA uses a phase space formed by trajectories, which represent the dynamics producing the time series. These three methods for producing the input matrix for the SVR proved successful, while the SVR-WT combination resulted in the highest coefficient of determination and the lowest mean absolute error.

Understand electronics

CERN Document Server

Bishop, Owen

2013-01-01

Understand Electronics provides a readable introduction to the exciting world of electronics for the student or enthusiast with little previous knowledge. The subject is treated with the minimum of mathematics and the book is extensively illustrated.This is an essential guide for the newcomer to electronics, and replaces the author's best-selling Beginner's Guide to Electronics.The step-by-step approach makes this book ideal for introductory courses such as the Intermediate GNVQ.

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

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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.

Application of Robust Regression and Bootstrap in Poductivity Analysis of GERD Variable in EU27

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Dagmar Blatná

2014-06-01

Full Text Available The GERD is one of Europe 2020 headline indicators being tracked within the Europe 2020 strategy. The headline indicator is the 3% target for the GERD to be reached within the EU by 2020. Eurostat defi nes “GERD” as total gross domestic expenditure on research and experimental development in a percentage of GDP. GERD depends on numerous factors of a general economic background, namely of employment, innovation and research, science and technology. The values of these indicators vary among the European countries, and consequently the occurrence of outliers can be anticipated in corresponding analyses. In such a case, a classical statistical approach – the least squares method – can be highly unreliable, the robust regression methods representing an acceptable and useful tool. The aim of the present paper is to demonstrate the advantages of robust regression and applicability of the bootstrap approach in regression based on both classical and robust methods.

The consequences of physical post-treatments (microwave and electron-beam) on food/packaging interactions: A physicochemical and toxicological approach.

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Riquet, A M; Breysse, C; Dahbi, L; Loriot, C; Severin, I; Chagnon, M C

2016-05-15

The safety of microwave and electron-beam treatments has been demonstrated, in regards to the formation of reaction products that could endanger human health. An integrated approach was used combining the potential toxicity of all the substances likely to migrate to their chemical characterizations. This approach was applied to polypropylene (PP) films prepared with a selection of additives. Components were identified by liquid and gas chromatography using a mass selective detector system. Their potential toxicity was assessed using three in vitro short-term bioassays and their migrations were carried out using a standards-based approach. After the electron-beam treatment some additives decomposed and there was a significant increase in the polyolefin oligomeric saturated hydrocarbons concentration. PP prepared with Irgafos 168 led to a significantly strong cytotoxic effect and PP prepared with Irganox 1076 induced a dose-dependant estrogenic effect in vitro. Migration values were low and below the detection limit of the analytical method applied. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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

2014-09-01

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

Ab initio electronic structure of quasi-two-dimensional materials: A “native” Gaussian–plane wave approach

Energy Technology Data Exchange (ETDEWEB)

Trevisanutto, Paolo E. [Graphene Research Centre and CA2DM, National University of Singapore, Singapore 117542, Singapore and Singapore Synchrotron Light Source, National University of Singapore, Singapore 117603 (Singapore); Vignale, Giovanni, E-mail: vignaleg@missouri.edu [Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211 (United States)

2016-05-28

Ab initio electronic structure calculations of two-dimensional layered structures are typically performed using codes that were developed for three-dimensional structures, which are periodic in all three directions. The introduction of a periodicity in the third direction (perpendicular to the layer) is completely artificial and may lead in some cases to spurious results and to difficulties in treating the action of external fields. In this paper we develop a new approach, which is “native” to quasi-2D materials, making use of basis function that are periodic in the plane, but atomic-like in the perpendicular direction. We show how some of the basic tools of ab initio electronic structure theory — density functional theory, GW approximation and Bethe-Salpeter equation — are implemented in the new basis. We argue that the new approach will be preferable to the conventional one in treating the peculiarities of layered materials, including the long range of the unscreened Coulomb interaction in insulators, and the effects of strain, corrugations, and external fields.

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.

The best of both worlds: Phylogenetic eigenvector regression and mapping

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José Alexandre Felizola Diniz Filho

2015-09-01

Full Text Available Eigenfunction analyses have been widely used to model patterns of autocorrelation in time, space and phylogeny. In a phylogenetic context, Diniz-Filho et al. (1998 proposed what they called Phylogenetic Eigenvector Regression (PVR, in which pairwise phylogenetic distances among species are submitted to a Principal Coordinate Analysis, and eigenvectors are then used as explanatory variables in regression, correlation or ANOVAs. More recently, a new approach called Phylogenetic Eigenvector Mapping (PEM was proposed, with the main advantage of explicitly incorporating a model-based warping in phylogenetic distance in which an Ornstein-Uhlenbeck (O-U process is fitted to data before eigenvector extraction. Here we compared PVR and PEM in respect to estimated phylogenetic signal, correlated evolution under alternative evolutionary models and phylogenetic imputation, using simulated data. Despite similarity between the two approaches, PEM has a slightly higher prediction ability and is more general than the original PVR. Even so, in a conceptual sense, PEM may provide a technique in the best of both worlds, combining the flexibility of data-driven and empirical eigenfunction analyses and the sounding insights provided by evolutionary models well known in comparative analyses.

The use of cognitive ability measures as explanatory variables in regression analysis.

Science.gov (United States)

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2012-12-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.

Entanglement transfer from electrons to photons in quantum dots: an open quantum system approach

International Nuclear Information System (INIS)

Budich, Jan C; Trauzettel, Bjoern

2010-01-01

We investigate entanglement transfer from a system of two spin-entangled electron-hole pairs, each placed in a separate single mode cavity, to the photons emitted due to cavity leakage. Dipole selection rules and a splitting between the light hole and the heavy hole subbands are the crucial ingredients establishing a one-to-one correspondence between electron spins and circular photon polarizations. To account for the measurement of the photons as well as dephasing effects, we choose a stochastic Schroedinger equation and a conditional master equation approach, respectively. The influence of interactions with the environment as well as asymmetries in the coherent couplings on the photon entanglement is analysed for two concrete measurement schemes. The first one is designed to violate the Clauser-Horne-Shimony-Holt (CHSH) inequality, while the second one employs the visibility of interference fringes to prove the entanglement of the photons. Because of the spatial separation of the entangled electronic system over two quantum dots, a successful verification of entangled photons emitted by this system would imply the detection of nonlocal spin entanglement of massive particles in a solid state structure.

Comparing spatial regression to random forests for large ...

Science.gov (United States)

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier.

Science.gov (United States)

Chen, Yi; Shao, Ying; Yan, Jie; Yuan, Ti-Fei; Qu, Yanwen; Lee, Elizabeth; Wang, Shuihua

2017-01-01

Alzheimer's disease patients are increasing rapidly every year. Scholars tend to use computer vision methods to develop automatic diagnosis system. (Background) In 2015, Gorji et al. proposed a novel method using pseudo Zernike moment. They tested four classifiers: learning vector quantization neural network, pattern recognition neural network trained by Levenberg-Marquardt, by resilient backpropagation, and by scaled conjugate gradient. This study presents an improved method by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Therefore, it can be used to detect Alzheimer's disease. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Neoclassical versus Frontier Production Models ? Testing for the Skewness of Regression Residuals

DEFF Research Database (Denmark)

Kuosmanen, T; Fosgerau, Mogens

2009-01-01

The empirical literature on production and cost functions is divided into two strands. The neoclassical approach concentrates on model parameters, while the frontier approach decomposes the disturbance term to a symmetric noise term and a positively skewed inefficiency term. We propose a theoreti......The empirical literature on production and cost functions is divided into two strands. The neoclassical approach concentrates on model parameters, while the frontier approach decomposes the disturbance term to a symmetric noise term and a positively skewed inefficiency term. We propose...... a theoretical justification for the skewness of the inefficiency term, arguing that this skewness is the key testable hypothesis of the frontier approach. We propose to test the regression residuals for skewness in order to distinguish the two competing approaches. Our test builds directly upon the asymmetry...

INVESTIGATION OF E-MAIL TRAFFIC BY USING ZERO-INFLATED REGRESSION MODELS

Directory of Open Access Journals (Sweden)

Yılmaz KAYA

2012-06-01

Full Text Available Based on count data obtained with a value of zero may be greater than anticipated. These types of data sets should be used to analyze by regression methods taking into account zero values. Zero- Inflated Poisson (ZIP, Zero-Inflated negative binomial (ZINB, Poisson Hurdle (PH, negative binomial Hurdle (NBH are more common approaches in modeling more zero value possessing dependent variables than expected. In the present study, the e-mail traffic of Yüzüncü Yıl University in 2009 spring semester was investigated. ZIP and ZINB, PH and NBH regression methods were applied on the data set because more zeros counting (78.9% were found in data set than expected. ZINB and NBH regression considered zero dispersion and overdispersion were found to be more accurate results due to overdispersion and zero dispersion in sending e-mail. ZINB is determined to be best model accordingto Vuong statistics and information criteria.

Seemingly Unrelated Regression Approach for GSTARIMA Model to Forecast Rain Fall Data in Malang Southern Region Districts

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Siti Choirun Nisak

2016-06-01

Full Text Available Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS assumes the variance-covariance matrix has a constant error ~(, but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR approach is used to accommodate the weakness of the OLS. SUR assumption is ~(, for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS. Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1(1,12,36,(0,(1-SUR with determination coefficient generated with the average of 57.726%.

A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

Science.gov (United States)

Madonna, Erica; Ginsbourger, David; Martius, Olivia

2018-05-01

In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April-September) in the period 2002-2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

Exploratory regression analysis: a tool for selecting models and determining predictor importance.

Science.gov (United States)

Braun, Michael T; Oswald, Frederick L

2011-06-01

Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.

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...

Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model