Unbiased Estimation of Functionals Under Random Censorship
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- Suzukawa Akio
- Graduate School of Economics and Business Administration, Hokkaido University
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Description
This paper is intended as an investigation of estimating functionals of a lifetime distribution F under right censorship. Functionals given by ∫ φdF, where φ’s are known F-integrable functions, are considered. The nonparametric maximum likelihood estimator of F is given by the Kaplan-Meier (KM) estimator Fn, where n is sample size. A natural estimator of ∫ φdF is a KM integral, ∫ φdFn. However, it is known that KM integrals have serious biases for unbounded φ’s. A representation of the KM integral in terms of the KM estimator of a censoring distribution is obtained. The representation may be useful not only to calculate the KM integral but also to characterize the KM integral from a point view of the censoring distribution and the biasedness. A class of unbiased estimators under the condition that the censoring distribution is known is considered, and the estimators are compared.
Journal
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- JOURNAL OF THE JAPAN STATISTICAL SOCIETY
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JOURNAL OF THE JAPAN STATISTICAL SOCIETY 34 (2), 153-172, 2004
THE JAPAN STATISTICAL SOCIETY
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Details 詳細情報について
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- CRID
- 1390282680264535552
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- NII Article ID
- 110003161358
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- NII Book ID
- AA1105098X
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- ISSN
- 13486365
- 18822754
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- MRID
- 2116753
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- NDL BIB ID
- 7208490
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed