PERFORMING LIKELIHOOD RATIO TESTS WITH MULTIPLY-IMPUTED DATA SETS
PERFORMING LIKELIHOOD RATIO TESTS WITH MULTIPLY-IMPUTED DATA SETS
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DOI：
10.1093/biomet/79.1.103
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发表时间：
1992-03-01
期刊：
影响因子：
2.7
通讯作者：
RUBIN, DB
中科院分区：
文献类型：
--
作者：
MENG, XL;RUBIN, DB
用于从相乘推算的数据获得显著水平的现有程序或者(I)需要访问完整的数据点估计和方差-协方差矩阵，当估计的维度很高时，实际上可能无法获得这些估计，或者(Ii)直接将p值与不太令人满意的结果组合。利用WALD和对数似然比检验统计量之间的众所周知的关系，我们提出了一种基于全数据对数似然比的方法。结果表明，对于任意数目的多重估计，该方法在大样本下与现有的基于点估计和方差-协方差矩阵的方法是等价的，但它只需要作为这些估计和完全数据的函数的完全数据对数似然比统计量的点估计和估计。因此，该方法对高度多参数不完全数据问题特别有吸引力，因为它不涉及任何矩阵的计算。
Existing procedures for obtaining significance levels from multiply-imputed data either (i) require access to the completed-data point estimates and variance-covariance matrices, which may not be available in practice when the dimensionality of the estimated is high, or (ii) directly combine p-values with less satisfactory results. Taking advantage of the well-known relationship between the Wald and log likelihood ratio test statistics, we propose a complete-data log likelihood ratio based procedure. It is shown that, for any number of multiple imputations, the proposed procedure is equivalent in large samples to the existing procedure based on the point estimates and the variance-covariance matrices, yet it only requires the point estimates and evaluations of the complete-data log likelihood ratio statistic as a function of these estimates and the completed data. The proposed procedure, therefore, is especially attractive with highly multiparameter incomplete-data problems since it does not involve the computation of any matrices.