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误分类二元数据的假设检验、分析与实验设计AI 解读

SUMMARY Consider a binary random variable having outcomes subject to misclassification errors where the probabilities of misclassification errors depend on outcome. The asymptotic relative efficiencies are derived for comparing two binomial populations, comparing stratified binomial populations and for paired comparisons. The probabilities of misclassification can vary with strata or be different for each pair. The criterion for optimum experimental designs is shown to be…

存在误分类的二元数据假设检验：分析与试验设计AI 解读

SUMMARY Consider a binary random variable having outcomes subject to misclassification errors where the probabilities of misclassification errors depend on outcome. The asymptotic relative efficiencies are derived for comparing two binomial populations, comparing stratified binomial populations and for paired comparisons. The probabilities of misclassification can vary with strata or be different for each pair. The criterion for optimum experimental designs is shown to be…

Nonparametric likelihood methods are developed for the analysis of partially censored data arising from a multistate stochastic process. It is assumed that the underlying process follows a semi-Markov model in which state changes form an embedded Markov chain and sojourn times are independent with distributions depending only on adjoining states. The general likelihood function for a set of partially censored observations is determined and maximized nonparametrically. The…

部分删失数据的半马尔可夫模型AI 解读

Nonparametric likelihood methods are developed for the analysis of partially censored data arising from a multistate stochastic process. It is assumed that the underlying process follows a semi-Markov model in which state changes form an embedded Markov chain and sojourn times are independent with distributions depending only on adjoining states. The general likelihood function for a set of partially censored observations is determined and maximized nonparametrically. The…

多个二乘二列联表的分析AI 解读

Consider data arranged into k 2×2 contingency tables. The principal result is the derivation of a statistical test for making an inference on whether each of the k contingency tables has the same relative risk. The test is based on a conditional reference set and can be regarded as an extension of the Fisher-Irwin treatment of a single 2 × 2 contingency table. Both exact and asymptotic procedures are presented.

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