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Analysis of doubly truncated data = ...

Dorre, Achim.

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Analysis of doubly truncated data = an introduction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analysis of doubly truncated data/ by Achim Dorre, Takeshi Emura.
Reminder of title:	an introduction /
Author:	Dorre, Achim.
other author:	Emura, Takeshi.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xvi, 109 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Mathematical statistics. -
Online resource:	https://doi.org/10.1007/978-981-13-6241-5
ISBN:	9789811362415

Analysis of doubly truncated data = an introduction /
Dorre, Achim.

Analysis of doubly truncated dataan introduction /[electronic resource] :by Achim Dorre, Takeshi Emura. - Singapore :Springer Singapore :2019. - xvi, 109 p. :ill., digital ;24 cm. - JSS research series in statistics,2364-0057. - JSS research series in statistics..

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.

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.

ISBN: 9789811362415

Standard No.: 10.1007/978-981-13-6241-5doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276 / .D865 2019

Dewey Class. No.: 519.5

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245 1 0 $a Analysis of doubly truncated data $h [electronic resource] : $b an introduction / $c by Achim Dorre, Takeshi Emura.
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300 $a xvi, 109 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Mathematical statistics. $3 516858
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