Analysis of Doubly Truncated Data An Introduction /

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 effective...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Dörre, Achim (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Emura, Takeshi (http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2019.
Έκδοση:1st ed. 2019.
Σειρά:JSS Research Series in Statistics,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Analysis of Doubly Truncated Data  |h [electronic resource] :  |b An Introduction /  |c by Achim Dörre, Takeshi Emura. 
250 |a 1st ed. 2019. 
264 1 |a Singapore :  |b Springer Singapore :  |b Imprint: Springer,  |c 2019. 
300 |a XVI, 109 p. 38 illus., 10 illus. in color.  |b online resource. 
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490 1 |a JSS Research Series in Statistics,  |x 2364-0057 
505 0 |a 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. 
520 |a 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. 
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