Download or read book Nonparametric Regression written by G. E. J. Smith and published by . This book was released on 1970 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonparametric Regression microform Analysis Multivariate Case written by Smith, G. E. J. (George E. John) and published by National Library of Canada. This book was released on 1970 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Nonparametric Methods with R written by Hannu Oja and published by Springer. This book was released on 2010-04-09 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Introduction to Nonparametric Regression written by K. Takezawa and published by John Wiley & Sons. This book was released on 2005-12-02 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: An easy-to-grasp introduction to nonparametric regression This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features: * Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods * Statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions * Mathematical equations that are accompanied by a clear explanation of how the equation was derived The first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts. With a learning-by-doing approach, each topical chapter includes thorough S-Plus? examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects. In addition, each chapter ends with a set of problems that test readers' grasp of key concepts and techniques and also prepares them for more advanced topics. This book is recommended as a textbook for undergraduate and graduate courses in nonparametric regression. Only a basic knowledge of linear algebra and statistics is required. In addition, this is an excellent resource for researchers and engineers in such fields as pattern recognition, speech understanding, and data mining. Practitioners who rely on nonparametric regression for analyzing data in the physical, biological, and social sciences, as well as in finance and economics, will find this an unparalleled resource.

Download or read book Multiple and Generalized Nonparametric Regression written by John Fox and published by SAGE Publications. This book was released on 2000-05-01 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book builds on John Fox′s previous volume in the QASS Series, Non Parametric Simple Regression. In this monograph readers learn to estimate and plot smooth functions when there are multiple independent variables. While regression analysis traces the dependence of the distribution of a response variable to see if it bears a particular (linear) relationship to one or more of the predictors, nonparametric regression analysis makes minimal assumptions about the form of relationship between the average response and the predictors. This makes nonparametric regression a more useful technique for analyzing data in which there are several predictors that may combine additively to influence the response. (An example could be something like birth order/gender/and temperament on achievement motivation). Unfortunately, researchers have not had accessible information on nonparametric regression analysis, until now. Beginning with presentation of nonparametric regression based on dividing the data into bins and averaging the response values in each bin, Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit. The book concludes with ways nonparametric regression can be generalized to logit, probit, and Poisson regression.

Download or read book Nonparametric Regression Methods for Longitudinal Data Analysis written by Hulin Wu and published by John Wiley & Sons. This book was released on 2006-05-12 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporates mixed-effects modeling techniques for more powerful and efficient methods This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented. With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis. Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices. With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.

Download or read book Alternative Methods of Regression written by David Birkes and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 sets real. Topics include: multi-response parameter estimation; models defined by systems of differential equations; and improved methods for presenting inferential results of nonlinear analysis. 1988 (0-471-81643-4) 365 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild ".[a] comprehensive and scholarly work.impressively thorough with attention given to every aspect of the modeling process." --Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range of estimation techniques including least squares, quasi-likelihood, and Bayesian methods, and discuss some of the problems associated with estimation. The book presents new and important material relating to the concept of curvature and its growing role in statistical inference. It also covers three useful classes of models --growth, compartmental, and multiphase --and emphasizes the limitations involved in fitting these models. Packed with examples and graphs, it offers statisticians, statistical consultants, and statistically oriented research scientists up-to-date access to their fields. 1989 (0-471-61760-1) 768 pp. Mathematical Programming in Statistics T. S. Arthanari and Yadolah Dodge "The authors have achieved their stated intention.in an outstanding and useful manner for both students and researchers.Contains a superb synthesis of references linked to the special topics and formulations by a succinct set of bibliographical notes.Should be in the hands of all system analysts and computer system architects." --Computing Reviews This unique book brings together most of the available results on applications of mathematical programming in statistics, and also develops the necessary statistical and programming theory and methods. 1981 (0-471-08073-X) 413 pp.

Download or read book A Distribution Free Theory of Nonparametric Regression written by László Györfi and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.

Download or read book Efficient Nonparametric and Semiparametric Regression Methods with Application in Case Control Studies written by Shahina Rahman and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression Analysis is one of the most important tools of statistics which is widely used in other scientific fields for projection and modeling of association between two variables. Nowadays with modern computing techniques and super high performance devices, regression analysis on multiple dimensions has become an important issue. Our task is to address the issue of modeling with no assumption on the mean and the variance structure and further with no assumption on the error distribution. In other words, we focus on developing robust semiparametric and nonparamteric regression problems. In modern genetic epidemiological association studies, it is often important to investigate the relationships among the potential covariates related to disease in case-control data, a study known as "Secondary Analysis". First we focus to model the association between the potential covariates in univariate dimension nonparametrically. Then we focus to model the association in mulivariate set up by assuming a convenient and popular multivariate semiparametric model, known as Single-Index Model. The secondary analysis of case-control studies is particularly challenging due to multiple reasons (a) the case-control sample is not a random sample, (b) the logistic intercept is practically not identifiable and (c) misspecification of error distribution leads to inconsistent results. For rare disease, controls (individual free of disease) are typically used for valid estimation. However, numerous publication are done to utilize the entire case-control sample (including the diseased individual) to increase the efficiency. Previous work in this context has either specified a fully parametric distribution for regression errors or specified a homoscedastic distribution for the regression errors or have assumed parametric forms on the regression mean. In the first chapter we focus on to predict an univariate covariate Y by another potential univariate covariate X neither by any parametric form on the mean function nor by any distributional assumption on error, hence addressing potential heteroscedasticity, a problem which has not been studied before. We develop a tilted Kernel based estimator which is a first attempt to model the mean function nonparametrically in secondary analysis. In the following chapters, we focus on i.i.d samples to model both the mean and variance function for predicting Y by multiple covariates X without assuming any form on the regression mean. In particular we model Y by a single-index model m(X^T [Lowercase theta symbol]), where [Lowercase theta symbol] is a single-index vector and m is unspecified. We also model the variance function by another flexible single index model. We develop a practical and readily applicable Bayesian methodology based on penalized spline and Markov Chain Monte Carlo (MCMC) both in i.i.d set up and in case-control set up. For efficient estimation, we model the error distribution by a Dirichlet process mixture models of Normals (DPMM). In numerical examples, we illustrate the finite sample performance of the posterior estimates for both i.i.d and for case-control set up. For single-index set up, in i.i.d case only one existing work based on local linear kernel method addresses modeling of the variance function. We found that our method based on DPMM vastly outperforms the other existing method in terms of mean square efficiency and computation stability. We develop the single-index modeling in secondary analysis to introduce flexible mean and variance function modeling in case-control studies, a problem which has not been studies before. We showed that our method is almost 2 times efficient than using only controls, which is typically used for many cases. We use the real data example from NIH-AARP study on breast cancer, from Colon Cancer Study on red meat consumption and from National Morbidity Air Pollution Study to illustrate the computational efficiency and stability of our methods. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155719

Download or read book Nonparametric Regression and Spline Smoothing written by Randall L. Eubank and published by CRC Press. This book was released on 1999-02-09 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for co

Download or read book Nonparametric Simple Regression written by John Fox and published by SAGE. This book was released on 2000-01-20 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric simple regression forms the basis for nonparametric multiple regression and directly supplies the building blocks for the type of nonparametric multiple regression called additive regression.

Download or read book Nonparametric Methods in Multivariate Analysis written by Madan Lal Puri and published by . This book was released on 1971 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Reduced Rank Regression written by Raja Velu and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the area of multivariate analysis, there are two broad themes that have emerged over time. The analysis typically involves exploring the variations in a set of interrelated variables or investigating the simultaneous relation ships between two or more sets of variables. In either case, the themes involve explicit modeling of the relationships or dimension-reduction of the sets of variables. The multivariate regression methodology and its variants are the preferred tools for the parametric modeling and descriptive tools such as principal components or canonical correlations are the tools used for addressing the dimension-reduction issues. Both act as complementary to each other and data analysts typically want to make use of these tools for a thorough analysis of multivariate data. A technique that combines the two broad themes in a natural fashion is the method of reduced-rank regres sion. This method starts with the classical multivariate regression model framework but recognizes the possibility for the reduction in the number of parameters through a restrietion on the rank of the regression coefficient matrix. This feature is attractive because regression methods, whether they are in the context of a single response variable or in the context of several response variables, are popular statistical tools. The technique of reduced rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.

Download or read book Introduction to Multivariate Analysis written by Sadanori Konishi and published by CRC Press. This book was released on 2014-06-06 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Select the Optimal Model for Interpreting Multivariate DataIntroduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedu

Download or read book Smooth Nonparametric Regression Analysis written by Gordon J. Johnston and published by . This book was released on 1979 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work investigates properties of the Watson type estimator of the regression function EY bar X = x of a bivariate random vector (X, Y). Pointwise weak consistency of the estimator is demonstrated. Sufficient conditions are given for asymptotic joint normality of the estimator taken at a finite number of distinct points, and for the uniform consistency of the estimator over a bounded interval. A large sample confidence band for the regression function, based on the estimator, is derived. (Author).

Download or read book Nonparametric Methods in Multivariate Analysis written by P.K. Sen and published by . This book was released on 1941 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Reduced Rank Regression written by Gregory C. Reinsel and published by Springer Nature. This book was released on 2022-11-30 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed. This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance. This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.