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190802 ||| eng |
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|a 9789811362415
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| 100 |
1 |
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|a Dörre, Achim
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| 245 |
0 |
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|a Analysis of Doubly Truncated Data
|h Elektronische Ressource
|b An Introduction
|c by Achim Dörre, Takeshi Emura
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| 250 |
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|a 1st ed. 2019
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2019, 2019
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| 300 |
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|a XVI, 109 p. 38 illus., 10 illus. in color
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Statistical Theory and Methods
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| 653 |
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|a Statistics
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| 653 |
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|a Biostatistics
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| 653 |
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|a Mathematical statistics—Data processing
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| 653 |
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|a Applied Statistics
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| 653 |
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|a Statistics and Computing
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| 653 |
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|a Biometry
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| 700 |
1 |
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|a Emura, Takeshi
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a JSS Research Series in Statistics
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-13-6241-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519.5
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| 520 |
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|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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