<p>“The aim of this book is to provide some fundamental ideas and methodologies for analysing doubly truncated data. ... The methodology of this book could be helpful to avoid a systematic bias in the contents of data due to loss of information.” (Nikita E. Ratanov, zbMATH 1434.62008, 2020)</p>
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.
Les mer
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.
Les mer
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.
Les mer
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.
Les mer
“The aim of this book is to provide some fundamental ideas and methodologies for analysing doubly truncated data. ... The methodology of this book could be helpful to avoid a systematic bias in the contents of data due to loss of information.” (Nikita E. Ratanov, zbMATH 1434.62008, 2020)
Les mer
Serves as an accessible introductory textbook on the analysis of doubly truncated data for students of statistics, mathematics, and econometrics Provides illustrative examples from biostatistics, economics, and other fields, with R codes to help readers analyze their data Presents clearer and more detailed explanations than those found in most journal papers
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Produktdetaljer
ISBN
9789811362408
Publisert
2019-05-22
Utgiver
Vendor
Springer Verlag, Singapore
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, UU, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Biographical note
Achim Dörre, University of Rostock
Takeshi Emura, Chang Gung University