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Gradient Projection for Nonparametric Maximum Likelihood Estimation with IntervalCensored Data

机译：Gradient projection for Nonparametric maximum Likelihood Estimation with IntervalCensored Data

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摘要

A well known way of computing the Non Parametric Maximum Likelihood Estimator(NPMLE) of a distribution function of censored observations is the EM algorithms, which is known as slow and hence not practicable, especially in simulation studies. In the case of interval censored observations, computing the NPMLE can be considered as a general strictly concave programming problem and a gradient projection method can be used to maximize a concave function subject to a number of constraints. This is worked out for interval censored observations with two or more censoring points. Some simulation results are given.

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作者
Bakker, D. M.;
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年度 1990
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总页数 25
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Data processing; Gradients; Maximum likelihood estimates; Observation; Algorithms; Computer programs; Computerized simulation; Distribution functions; Theorems;

机译：数据处理;梯度;最大似然估计;观测;算法;计算机程序;计算机模拟;分布函数;定理;

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Gradient Projection for Nonparametric Maximum Likelihood Estimation with IntervalCensored Data

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