Empirical Likelihood Based Inferences for Partially Linear Models with Missing Covariates.
Empirical Likelihood Based Inferences for Partially Linear Models with Missing Covariates.
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DOI：
10.1111/j.1467-842x.2008.00521.x
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发表时间：
2008-12
影响因子：
1.1
通讯作者：
Qin Y
中科院分区：
文献类型：
--
作者：
Liang H;Qin Y
本文考虑当线性协变量 X 缺失且缺失概率 π 取决于 (Y, Z) 时，部分线性模型 Y = XTμ + ν(Z) + ε 的统计推断。我们提出基于经验似然的统计来构建 β 和 ν(z) 的置信区域。结果统计数据显示为渐近卡方分布。通过模拟实验评估所提出的统计数据的有限样本性能。所提出的方法应用于艾滋病临床试验的数据集。
This paper considers statistical inference for partially linear models Y = XTμ + ν(Z) + ε when the linear covariate X is missing with missing probability π depending upon (Y, Z). We propose empirical likelihood based statistics to construct confidence regions for β and ν(z). The resulting statistics are shown to be asymptotically chi-squared distributed. Finite sample performance of the proposed statistics is assessed by simulation experiments. The proposed methods are applied to a data set from an AIDS clinical trial.