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Non-parametric hypothesis testing and confidence intervals with doubly censored data.

Kun Chen·Mai Zhou
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Abstract

The non-parametric maximum likelihood estimator (NPMLE) of the distribution function with doubly censored data can be computed using the self-consistent algorithm (Tumbull, 1974). We extend the self-consistent algorithm to include a constraint on the NPMLE. We then show how to construct confidence intervals and test hypotheses based on the NPMLE via the empirical likelihood ratio. Finally, we present some numerical comparisons of the performance of the above method with another method that makes use of the influence functions.

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AlgorithmsConfidence IntervalsData Interpretation, StatisticalHumansLife TablesLikelihood FunctionsResearch DesignSensitivity and SpecificityJournal ArticleReview

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Kun Chen, Mai Zhou. 2003. Non-parametric hypothesis testing and confidence intervals with doubly censored data.. https://doi.org/10.1023/a%3A1021834206327

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