Analysis of Doubly Truncated Data

An Introduction

  • Achim Dörre
  • Takeshi Emura

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Also part of the JSS Research Series in Statistics book sub series (JSSRES)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Achim Dörre, Takeshi Emura
    Pages 1-18
  3. Achim Dörre, Takeshi Emura
    Pages 19-40
  4. Achim Dörre, Takeshi Emura
    Pages 41-62
  5. Achim Dörre, Takeshi Emura
    Pages 63-74
  6. Achim Dörre, Takeshi Emura
    Pages 75-86
  7. Back Matter
    Pages 87-109

About this book


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.


Survival Analysis Left-truncation Double-truncation Lifetime Distribution Biased Sampling Exponential Family Maximum Likelihood Estimation

Authors and affiliations

  • Achim Dörre
    • 1
  • Takeshi Emura
    • 2
  1. 1.Department of EconomicsUniversity of RostockRostockGermany
  2. 2.Graduate Institute of StatisticsNational Central UniversityTaoyuan CityTaiwan

Bibliographic information

  • DOI
  • Copyright Information The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-981-13-6240-8
  • Online ISBN 978-981-13-6241-5
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site
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