Simultaneous Inference in Regression

AvWei Liu

Häftad, Engelska, 2017

Del i serien Chapman & Hall/CRC Monographs on Statistics and Applied Probability

1 267 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Fler format och utgåvor

E-bok

1 491 kr

Inbunden

3 373 kr

E-bok

1 480 kr

Beskrivning

Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods of parameter estimation and hypothesis testing. Simultaneous Inference in Regression provides a thorough overview of the construction methods and applications of simultaneous confidence bands for various inferential purposes. It supplies examples and MATLAB® programs that make it easy to apply the methods to your own data analysis. The MATLAB programs, along with color figures, are available for download on www.personal.soton.ac.uk/wl/mybook.htmlMost of the book focuses on normal-error linear regression models. The author presents simultaneous confidence bands for a simple regression line, a multiple linear regression model, and polynomial regression models. He also uses simultaneous confidence bands to assess part of a multiple linear regression model with the zero function, to compare two regression models, and to evaluate more than two regression models. The final chapter demonstrates the use of simultaneous confidence bands in generalized linear regression models, such as logistic regression models. This book shows how to employ simultaneous confidence bands to make useful inferences in regression analysis. The topics discussed can be extended to functions other than parametric regression functions, offering novel opportunities for research beyond linear regression models.

Produktinformation

Utgivningsdatum:2017-06-13
Mått:156 x 234 x 13 mm
Vikt:550 g
Format:Häftad
Språk:Engelska
Serie:Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Antal sidor:292
Förlag:Taylor & Francis Ltd
ISBN:9781138111684

Utforska kategorier

Mer om författaren

Wei Liu is a professor of statistics at the University of Southampton, UK. Dr. Liu has published more than 80 papers in peer-reviewed journals, including Annals of Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society, Biometrika, and Biometrics. His research encompasses multiple comparison, simultaneous inference, and sequential methods.

Recensioner i media

I like in particular this book since almost all results are given with proofs and that these proofs are easy to understand having some knowledge in linear models. It is especially helpful that the geometric ideas behind the proofs are worked out and presented with many illustrations. Together with the comprehensive review of the published literature and the presentation of some unsolved problems, this book is very valuable for researchers. It is also very recommendable for practitioners since the ideas of the concepts are worked out clearly and many examples and figures are presented where the confidence bands are applied to real data. MATLAB programs for all methods can be downloaded from the author’s website.—Christine Müller, Biometrical Journal 53 (2011)This book provides a comprehensive discussion of methods for determining simultaneous confidence bands in regression. … The book provides a valuable up-to-date review of work in this area. —David J. Hand, International Statistical Review (2011), 79… definitely fills a significant niche, providing practitioners with powerful inference tools for parametric regression and stimulating further research in this important area.— Tatyana Krivobokova, Georg-August- Univer,citat Gottingen, Royal Statistics Society, February 2012

Innehållsförteckning

Introduction to Linear Regression Analysis. Confidence Bands for One Simple Regression Model. Confidence Bands for One Multiple Regression Model. Assessing Part of a Regression Model. Comparison of Two Regression Models. Comparison of More Than Two Regression Models. Confidence Bands for Polynomial Regression. Confidence Bands for Logistic Regression. Appendices. Bibliography. Index.