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Record Nr. |
UNINA9910350247703321 |
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Autore |
Dörre Achim |
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Titolo |
Analysis of Doubly Truncated Data : An Introduction / / by Achim Dörre, Takeshi Emura |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XVI, 109 p. 38 illus., 10 illus. in color.) |
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Collana |
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JSS Research Series in Statistics, , 2364-0057 |
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Disciplina |
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Soggetti |
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Statistics |
Biostatistics |
Statistical Theory and Methods |
Applied Statistics |
Statistics and Computing/Statistics Programs |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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, |
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mathematics, econometrics, and other fields. |
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