Park T
Department of Statistics, Hankuk University of Foreign Studies, Kyungki-Do, Korea.
Biometrics. 1998 Dec;54(4):1579-90.
Log-linear models have been used to adjust for nonresponse when categorical outcomes are subject to nonignorable nonresponse. The log models are fitted to the data in an augmented frequency table in which one index corresponds to whether the subject is a respondent. Park and Brown (1994, Journal of the American Statistical Association 89, 44-52) proposed a pseudo-Bayesian method that has the effect of smoothing the unobserved cell frequencies. Their approach assigns prior observations only to the unobserved cells. Their method was shown to perform better than the maximum likelihood method, which can produce unstable boundary estimates. Generalizing their approach, we propose a new approach that assigns prior observations to both observed and unobserved cells. Through a simulation study, we compare the proposed approach with Park and Brown's and the maximum likelihood approach.
当分类结果存在不可忽略的无应答情况时，对数线性模型已被用于对无应答进行调整。对数模型适用于一个扩充频率表中的数据，其中一个指标对应于该对象是否为应答者。帕克和布朗（1994年，《美国统计协会杂志》89卷，44 - 52页）提出了一种伪贝叶斯方法，该方法具有平滑未观察到的单元格频率的效果。他们的方法仅将先验观测值分配给未观察到的单元格。结果表明，他们的方法比最大似然法表现更好，最大似然法可能会产生不稳定的边界估计。在推广他们方法的基础上，我们提出了一种新方法，该方法将先验观测值同时分配给观察到的和未观察到的单元格。通过模拟研究，我们将所提出的方法与帕克和布朗的方法以及最大似然法进行了比较。
