The IOS test for model misspecification
The IOS test for model misspecification
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DOI：
10.1198/016214504000000214
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发表时间：
2004-03-01
影响因子：
3.7
通讯作者：
Boos, DD
中科院分区：
文献类型：
--
作者：
Presnell, B;Boos, DD
提出了一种基于样本内、样本外似然比的模型误设检验方法。该测试是广泛适用的，在简单的问题，近似众所周知的，直观的方法。使用刀切影响曲线近似，它表明，检验统计量可以被视为渐近的信息矩阵的两个估计之间的乘法对比，这两个估计是一致的正确的模型规格。这种近似用于表明统计量是渐近正态分布的，尽管建议使用参数自助法计算p值。由此产生的方法证明了各种例子和模拟涉及离散和连续数据。
A new test of model misspecification is proposed, based on the ratio of in-sample and out-of-sample likelihoods. The test is broadly applicable and, in simple problems, approximates well-known, intuitive methods. Using jackknife influence curve approximations, it is shown that the test statistic can be viewed asymptotically as a multiplicative contrast between two estimates of the information matrix, both of which are consistent under correct model specification. This approximation is used to show that the statistic is asymptotically normally distributed, although it is suggested that p values be computed using the parametric bootstrap. The resulting methodology is demonstrated with various examples and simulations involving both discrete and continuous data.