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针对右删失数据下线性混合效应模型的统计推断问题，提出了基于Mallows准则的模型平均方法 .该方法采用逆概率加权处理删失偏倚，并通过最小化Mallows准则来选取最优权重，证明了该模型平均估计量具有渐近最优性.此外，针对随机效应协方差矩阵和误差项方差未知的实际场景，给出其估计方法，证明采用可行加权最小二乘估计量进行平均时仍保持渐近最优性.模拟结果表明，该方法在处理删失混合效应模型时，相较于其他方法具有更高的估计精度和更好的模型稳定性.最后，通过对aids数据集的分析验证了该方法优异的预测性能.
Abstract：This paper addresses statistical inference for linear mixed-effects models with right-censored data by proposing a Mallows model averaging method.The approach employs inverse probability weighting to adjust for censoring bias and selects optimal weights by minimizing the Mallows criterion.We establish the asymptotic optimality of the resulting estimator.Furthermore,for practical scenarios with unknown covariance matrices for random effects and error terms,we provide consistent estimation methods and prove that the asymptotic optimality is preserved even when using feasible weighted least squares estimators. Simulation results demonstrate that the proposed method achieves higher estimation accuracy and better stability than existing alternatives.Its superior predictive performance is further validated through an analysis of an AIDS dataset.
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基本信息:
DOI：10.20212/j.issn.1008-2441.2025.06.002
中图分类号:O212.1
引用信息:
[1]李楚,田茂再.基于逆概率加权与Mallows模型平均的删失混合效应模型统计推断[J].鞍山师范学院学报,2025,27(06):9-17.DOI:10.20212/j.issn.1008-2441.2025.06.002.
基金信息:
北京市自然科学基金项目（1242005）
2025-12-20
2025-12-20