Informational dynamics of censored observations
Informational dynamics of censored observations
复制标题
审查观察的信息动态
DOI：
10.1287/mnsc.37.11.1390
复制
发表时间：
1991
影响因子：
5.4
通讯作者：
M. Freimer
中科院分区：
文献类型：
--
作者：
David J. Braden;M. Freimer
随机模型的分析往往是非常复杂的，如果有随机变量的截尾观测。本文描述了分布族的特征，这些分布族有助于分析这些模型。我们的主要动机是为从业者提供分布选择方面的指导：如果建模者认为我们描述的族中没有任何成员是合理的近似，那么如果他的数据包括审查观察，他几乎肯定会遇到严重的分析和计算问题。我们刻画了一类分布族，对于这类分布族，存在纯删失观测的固定维充分统计量。我们还描述了这个家庭的一个重要子集，适合的情况下，数据包括删失和精确的观察。我们推导出相应的预测分布，使用任意先验，并提出了一些一般性的结果预测分布之间的随机优势的参数的先验。我们还分析了离散和混合随机变量的情况。
The analysis of stochastic models is often greatly complicated if there are censored observations of the random variables. This paper characterizes families of distributions which help keep tractable the analysis of such models. Our primary motivation is to provide guidance to practitioners in the selection of distributions: If a modeler feels that no member of the families we characterize is a reasonable approximation, then he will almost surely encounter serious analytic and computational problems if his data include censored observations. We characterize a family of distributions for which there exist fixed-dimensional sufficient statistics of purely censored observations. We also characterize an important subset of this family, appropriate for situations where data include both censored and exact observations. We derive the corresponding predictive distributions using arbitrary priors and present some general results relating stochastic dominance among predictive distributions to the parameters of the prior. We also analyze the cases of discrete and mixed random variables.