Estimation and empirical likelihood for single-index models with missing data in the covariates
Estimation and empirical likelihood for single-index models with missing data in the covariates
复制标题
协变量中缺失数据的单指数模型的估计和经验可能性
DOI：
10.1016/j.csda.2013.06.017
复制
发表时间：
2013-12-01
影响因子：
1.8
通讯作者：
Xue, Liugen
中科院分区：
文献类型：
--
作者：
Xue, Liugen
研究了协变量缺失的单指标模型的估计和经验似然。构造了协变量缺失时指标系数的广义估计方程估计，并得到了其渐近分布。链接函数的局部线性估计器达到最优收敛速度。利用偏差校正和逆选择概率加权方法，提出了一类经验似然比，使得每一类比都是渐近卡方的。仿真研究表明，所提出的方法是可比的覆盖概率和置信区间（区域）的平均长度（面积）。举例说明了一个真实的数据集。(C)2013爱思唯尔有限公司版权所有。
The estimation and empirical likelihood for single-index models with missing covariates are studied. A generalized estimating equations estimator for index coefficients with missing covariates is constructed, and its asymptotic distribution is obtained. The local linear estimator for link function achieves optimal convergence rate. By using the bias-correction and inverse selection probability weighted methods, a class of empirical likelihood ratios is proposed such that each of our class of ratios is asymptotically chi-squared. A simulation study indicates that the proposed methods are comparable in terms of coverage probabilities and average lengths (areas) of confidence intervals (regions). An example of a real data set is illustrated. (C) 2013 Elsevier B.V. All rights reserved.