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基于提名抽样的分布函数估计AI 解读

Nomination sampling is a sampling process in which every observation is the maximum of a random sample from some population. Assuming that all samples are taken from a single underlying distribution F, data may be viewed as consisting of pairs (Xi , Ki ), where Ki is the size of the ith sample and, given Ki = ki, Xi is distributed according to F ki. Willemain (1980), who discussed nomination sampling in the context of health care delivery, proposed an estimator for the…

具有正依赖的多元二元数据建模与推断AI 解读

A model is proposed for multivariate binary data that incorporates positive dependence among components in a natural way. The model is derived from reliability-theoretic concepts, but is regarded as appropriate for analysis of multivariate binary data in any field when positive dependence is an appropriate assumption. Maximum likelihood estimation by iterative solution of likelihood equations is discussed for the general model, and asymptotically efficient estimates are…

具有正相依性的多元二元数据建模与推断AI 解读

A model is proposed for multivariate binary data that incorporates positive dependence among components in a natural way. The model is derived from reliability-theoretic concepts, but is regarded as appropriate for analysis of multivariate binary data in any field when positive dependence is an appropriate assumption. Maximum likelihood estimation by iterative solution of likelihood equations is discussed for the general model, and asymptotically efficient estimates are…

离散多元冲击模型的最大似然估计AI 解读

A k-variate Bernoulli distribution with k + 1 parameters is obtained as a shock model in which shocks are fatal to single components only or to all components simultaneously in a k-component system. The maximum likelihood estimator (MLE) for model parameters is fully characterized. In the most complex case, the MLE is displayed as a simple function of the smallest positive root of a kth degree polynomial. An iterative scheme that converges monotonically to the desired root…