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摘要翻译：
本文从存在偏差和截尾的观测数据出发，导出了分布函数的非参数极大似然估计(NPMLE)。NPMLE是由一个简单的EM算法得到的，该算法是Vardi(Biometrika，1989)提出的用于尺寸偏置数据的算法的扩展。讨论了该算法在多种模型中的应用，并通过仿真比较了该估计器与乘积极限估计器(PLE)的性能。一个示例演示了NPMLE对PLE不合适的数据的效用。
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英文标题：
《Nonparametric estimation of a distribution function under biased
sampling and censoring》
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作者：
Micha Mandel
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最新提交年份：
2007
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分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
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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PDF链接：
https://arxiv.org/pdf/708.1061
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