Meta分析是一种通过对多个独立研究结果进行系统的整合、分析和综合,以得出更为准确、全面的结论的统计方法.在Meta分析的预测过程中,为了解决模型选择存在的不确定性问题,文章提出了一种基于Mallows准则的模型平均(MMA)方法,并证明了其具有渐近最优性.最后,对文章所提出的MMA估计与Jackknife模型平均(JMA)、S-AIC和S-BIC信息准则下的模型平均估计进行了模拟研究,并应用于卡介苗疫苗数据的实例分析,结果均表明MMA优于其他模型平均估计.
Abstract
Meta-analysis is a statistical method that systematically integrates, analyzes and synthesizes the results of multiple independent studies to reach more accurate and comprehensive conclusions. To solve model uncertainty in the prediction for meta-analysis, an optimal model averaging prediction method is proposed based on Mallows criterion, and the optimality of Mallows model averaging(MMA) estimator under square loss is discussed. Finally, simulation studies are conducted to evaluate and compare the performance of MMA, Jackknife model average(JMA), S-AIC and S-BIC model average estimation under information criteria, and all methods are applied to analyze the data set of BCG vaccine for illustration. The results show that the MMA estimation is superior to other model average estimations in prediction regardless of whether the variance and sample size are large or small.
关键词
Meta分析 /
模型平均 /
Mallows准则 /
渐近最优性
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Key words
Meta-analysis /
model averaging /
Mallows criterion /
asymptomatic optimality
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中图分类号：
62F10
62F12
62P10
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脚注
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基金
国家社会科学基金(23BTJ064)资助课题.
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