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＜電子ブック＞
Analysis of Doubly Truncated Data : An Introduction / by Achim Dörre, Takeshi Emura
(JSS Research Series in Statistics. ISSN:23640065)

版	1st ed. 2019.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2019
大きさ	XVI, 109 p. 38 illus., 10 illus. in color : online resource
著者標目	*Dörre, Achim author Emura, Takeshi author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Biometry LCSH:Mathematical statistics—Data processing FREE:Statistical Theory and Methods FREE:Applied Statistics FREE:Biostatistics FREE:Statistics and Computing
一般注記	Chapter 1: Introduction to double-truncation -- Chapter 2: Parametric inference under special exponential family -- Chapter 3: Parametric inference under location-scale family -- Chapter 4: Bayes inference -- Chapter 5: Nonparametric inference -- Chapter 6: Linear regression -- Appendix A: Data (if German company data are available) -- Appendix B: R codes for inference under exponential family -- Appendix C: R codes for inference under location-scale family -- Appendix D: R codes for Bayes inference -- Appendix E: R codes for linear regression This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields HTTP:URL=https://doi.org/10.1007/978-981-13-6241-5

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