Bayesian Estimation of Finite Population Parameters in Categorical Data Models Incorporating Order Restrictions
提出一种贝叶斯方法，在总体调查中当回归模型不可用时，利用分类数据模型并引入顺序限制（如单峰或多峰）作为先验信息，通过蒙特卡洛积分估计有限总体均值。
SUMMARY This note describes a Bayesian method for estimation of finite population parameters in general population surveys where acceptable regression-type models are typically unavailable. A categorical data model is adopted as in Ericson (1969, Section 4). However, specifications of smoothness are incorporated into the prior distribution. These smoothness conditions are expressed as unimodal or, possibly, multi-modal order relations among the category probabilities. Emphasis is placed on posterior inference about the finite population mean. Of independent interest is the methodology for evaluating the posterior moments and probabilities using Monte Carlo integration with importance sampling.