Smoothed quantile regression with nonignorable dropouts

Abstract

In this paper, we adopt a three-stage estimation procedure and statistical inference methods for quantile regression (QR) based on empirical likelihood (EL) approach with nonignorable dropouts. In the first stage, we consider a parametric model on the dropout propensity of response and handle the parameter identifiability issue by using nonresponse instrument. With the estimated dropout propensity, in the second stage the inverse probability weighting and kernel smoothing methods are applied to construct the bias-corrected and smoothed generalized estimating equations for nonignorable dropouts. In the third stage, borrowing the matrix expansion idea of quadratic inference function, we obtain the proposed estimators that can accommodate the within-subject correlations and improve the estimation efficiency simultaneously. A class of improved estimators and their confidence regions for QR coefficient are derived. Further, the penalized EL method and algorithm for variable selection are investigated. Simulation studies and a real example on HIV-CD4 data set are also provided to show the performance of the proposed estimators.