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信息性一般删失下分类数据的贝叶斯方法AI 解读

This paper develops a Bayesian approach to the problem of incomplete categorical data informatively censored where the reported sets are not restricted to follow any specific pattern. It generalises that introduced by Paulino & Pereira (1992) in not requiring a censoring pattern by partitions of the set of sampling categories. Some extensions are also discussed.

有限封闭总体规模的精确极大似然估计札记AI 解读

Using data obtained by the general capture-recapture sequential sampling process, an analytical expression for the maximum likelihood estimate of the population size is introduced. It is shown that the bounded likelihood functions have at most two maxima. For the simple one-to-one case the estimate is unique.