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|a 10.1007/978-981-13-6241-5
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|a Dörre, Achim.
|e author.
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|4 http://id.loc.gov/vocabulary/relators/aut
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1 |
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|a Analysis of Doubly Truncated Data
|h [electronic resource] :
|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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| 264 |
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1 |
|a Singapore :
|b Springer Singapore :
|b Imprint: Springer,
|c 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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|a JSS Research Series in Statistics,
|x 2364-0057
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|a Springer eBook Collection
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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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| 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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| 590 |
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|a Loaded electronically.
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| 590 |
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|a Electronic access restricted to members of the Holy Cross Community.
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|a Statistics .
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|a Biostatistics.
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| 690 |
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|a Electronic resources (E-books)
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|a Emura, Takeshi.
|e author.
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