Analysis of Doubly Truncated Data An Introduction
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 effective...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Singapore
Springer Nature Singapore
2019, 2019
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| Edition: | 1st ed. 2019 |
| Series: | JSS Research Series in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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