Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies
Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies
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DOI：
10.1016/j.csda.2003.12.009
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发表时间：
2004-10-01
影响因子：
1.8
通讯作者：
Ohman-Strickland, P
中科院分区：
文献类型：
--
作者：
Agresti, A;Caffo, B;Ohman-Strickland, P
本笔记显示了三种情况，其中随机效应的参数分布与真实分布有很大的不同，这可能导致相当大的效率损失。对于二元响应数据的两个简单模型，我们研究了当真实分布可能远离正态分布时，假设正态或使用随机效应的非参数拟合程序的影响。虽然通常随机效应分布的选择对预测结果概率的效率影响不大，但当真实分布是具有大方差成分的两点混合分布时，正态方法受到影响。同样，对于一个简单的生存模型，假设弱点分布是一个伽玛分布，而真实的分布是两点混合，这将导致在预测弱点时效率的相当大的损失。最后讨论了解决潜在效率损失问题的可能途径，并对今后的研究提出了建议。(C) 2003 Elsevier B.V.版权所有
This note shows three cases in which a considerable loss of efficiency can result from assuming a parametric distribution for a random effect that is substantially different from the true distribution. For two simple models for binary response data, we studied the effects of assuming normality or of using a nonparametric fitting procedure for random effects, when the true distribution is potentially far from normal. Although usually the choice of random effects distribution has little effect on efficiency of predicting outcome probabilities, the normal approach suffered when the true distribution was a two-point mixture with a large variance component. Likewise, for a simple survival model, assuming a gamma distribution for the frailty distribution when the true one was a two-point mixture resulted in considerable loss of efficiency in predicting the frailties. The paper concludes with a discussion of possible ways of addressing the problem of potential efficiency loss, and makes suggestions for future research. (C) 2003 Elsevier B.V. All rights reserved.