Nonparametric Statistical Inference - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Statistical Inference write by Jean Dickinson Gibbons. This book was released on 2010-07-26. Nonparametric Statistical Inference available in PDF, EPUB and Kindle. Proven Material for a Course on the Introduction to the Theory and/or on the Applications of Classical Nonparametric Methods Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material. New to the Fifth Edition Updated and revised contents based on recent journal articles in the literature A new section in the chapter on goodness-of-fit tests A new chapter that offers practical guidance on how to choose among the various nonparametric procedures covered Additional problems and examples Improved computer figures This classic, best-selling statistics book continues to cover the most commonly used nonparametric procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXact and compares these examples with corresponding hand calculations. The appendix includes a collection of tables required for solving the data-oriented problems. Nonparametric Statistical Inference, Fifth Edition provides in-depth yet accessible coverage of the theory and methods of nonparametric statistical inference procedures. It takes a practical approach that draws on scores of examples and problems and minimizes the theorem-proof format. Jean Dickinson Gibbons was recently interviewed regarding her generous pledge to Virginia Tech.
Parametric and Nonparametric Inference from Record-Breaking Data
Parametric and Nonparametric Inference from Record-Breaking Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parametric and Nonparametric Inference from Record-Breaking Data write by Sneh Gulati. This book was released on 2003-01-27. Parametric and Nonparametric Inference from Record-Breaking Data available in PDF, EPUB and Kindle. By providing a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, this book treats the area of nonparametric function estimation from such data in detail. Its main purpose is to fill this void on general inference from record values. Statisticians, mathematicians, and engineers will find the book useful as a research reference. It can also serve as part of a graduate-level statistics or mathematics course.
Nonparametric Inference
Nonparametric Inference - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Inference write by Z. Govindarajulu. This book was released on 2007. Nonparametric Inference available in PDF, EPUB and Kindle. This book provides a solid foundation on nonparametric inference for students taking a graduate course in nonparametric statistics and serves as an easily accessible source for researchers in the area.With the exception of some sections requiring familiarity with measure theory, readers with an advanced calculus background will be comfortable with the material.
All of Nonparametric Statistics
All of Nonparametric Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook All of Nonparametric Statistics write by Larry Wasserman. This book was released on 2006-09-10. All of Nonparametric Statistics available in PDF, EPUB and Kindle. This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.
Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications
Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications write by Chiara Brombin. This book was released on 2016-02-19. Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications available in PDF, EPUB and Kindle. This book considers specific inferential issues arising from the analysis of dynamic shapes with the attempt to solve the problems at hand using probability models and nonparametric tests. The models are simple to understand and interpret and provide a useful tool to describe the global dynamics of the landmark configurations. However, because of the non-Euclidean nature of shape spaces, distributions in shape spaces are not straightforward to obtain. The book explores the use of the Gaussian distribution in the configuration space, with similarity transformations integrated out. Specifically, it works with the offset-normal shape distribution as a probability model for statistical inference on a sample of a temporal sequence of landmark configurations. This enables inference for Gaussian processes from configurations onto the shape space. The book is divided in two parts, with the first three chapters covering material on the offset-normal shape distribution, and the remaining chapters covering the theory of NonParametric Combination (NPC) tests. The chapters offer a collection of applications which are bound together by the theme of this book. They refer to the analysis of data from the FG-NET (Face and Gesture Recognition Research Network) database with facial expressions. For these data, it may be desirable to provide a description of the dynamics of the expressions, or testing whether there is a difference between the dynamics of two facial expressions or testing which of the landmarks are more informative in explaining the pattern of an expression.
