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arXiv · 0708.1061

Nonparametric estimation of a distribution function under biased sampling and censoring

Micha Mandel
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Abstract

This paper derives the nonparametric maximum likelihood estimator (NPMLE) of a distribution function from observations which are subject to both bias and censoring. The NPMLE is obtained by a simple EM algorithm which is an extension of the algorithm suggested by Vardi (Biometrika, 1989) for size biased data. Application of the algorithm to many models is discussed and a simulation study compares the estimator's performance to that of the product-limit estimator (PLE). An example demonstrates the utility of the NPMLE to data where the PLE is inappropriate.

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Micha Mandel. 2007-08-08. Nonparametric estimation of a distribution function under biased sampling and censoring. https://doi.org/10.1214/074921707000000175

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