Statistical inference on residual life / Jong-Hyeon Jeong.
Material type:
TextSeries: Statistics for biology and healthPublication details: New York : Springer, 2014.Description: xi, 201 p. : illustrations (some color) ; 24 cmISBN: - 9781493900046
- 000SA.1 23 J54
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.1 J54 (Browse shelf(Opens below)) | Available | 136420 |
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| 000SA.1 G427 Robust minimum divergence inference using density power divergence and its extensions : | 000SA.1 H474 Applied statistical inference : | 000SA.1 In61 Perspectives in statistical sciences | 000SA.1 J54 Statistical inference on residual life / | 000SA.1 K49 Statistical methods for handling incomplete data / | 000SA.1 K49 Statistical methods for handling incomplete data / | 000SA.1 M494 Statistical intervals : |
Includes bibliographical references and index.
1. Introduction --
2. Inference on Mean Residual Life --
3. Quantile Residual Life --
4. Quantile Residual Life under Competing Risks --
5. Other Methods for Inference on Quantiles --
6. Study Design based on Quantile (Residual) Life --
Appendix: R codes--
References--
About the author--
Index.
This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians.
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