协变量缺失情形下的逆概率加权众数回归估计

Inverse Probability Weighted Mode Regression Estimation with Missing Covariates

数据缺失在实际应用中普遍存在,数据缺失会降低研究效率,导致参数估计有偏.在协变量随机缺失(MAR)的假定下,本文基于众数回归和逆概率加权估计方法对线性模型进行参数估计.该方法结合参数Logistic回归和非参数Nadaraya-Watson估计两种倾向得分估计方法,分别构建IPWM-L估计量和IPWM-NW估计量.模拟研究和实例分析表明,众数回归模型比均值回归模型更具稳健性,逆概率加权众数(IPWM)估计方法在缺失数据下表现出了更好的拟合效果,与IPWM-L估计量相比,IPWM-NW估计量更稳健.

Missing datas are common in practical applications.Missing datas will reduce research efficiency and lead to biased parameter estimation.Under the assumption that the covariates are missing at random(MAR),this paper estimates the parameters of linear models based on mode regression and inverse probability weighted estimation.This method combines two propensity score estimation methods,parametric logistic regression and nonparametric Nadaraya-Watson estimation,to construct IPWM-L estimator and IPWM-NW estimator respectively.Simulation research and case analysis show that the mode regression model is more robust than the mean regression model,and the inverse probability weighted mode(IPWM)estimation method shows better fitting effect under missing data.Compared with the IPWM-L estimator,the IPWM-NW estimator is more robust.