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Title Analysis of Doubly Truncated Data [electronic resource] : An Introduction / by Achim Dörre, Takeshi Emura Singapore : Springer Singapore : Imprint: Springer, 2019 1st ed. 2019 https://doi.org/10.1007/978-981-13-6241-5 XVI, 109 p. 38 illus., 10 illus. in color. online resource

SUMMARY

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

CONTENT

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

Mathematical statistics Statistics Statistical methods Statistical Theory and Methods. http://scigraph.springernature.com/things/product-market-codes/S11001 Applied Statistics. http://scigraph.springernature.com/things/product-market-codes/S17000 Biostatistics. http://scigraph.springernature.com/things/product-market-codes/L15020 Statistics and Computing/Statistics Programs. http://scigraph.springernature.com/things/product-market-codes/S12008

Location

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