Rank-based Estimating Equation with non-ignorable missing responses via empirical likelihood
Document Type
Lecture
Publication Date
3-7-2024
Abstract
In this talk, we will consider a general regression model with responses missing not at random. We will consider a rank-based estimating equation of the regression parameter from which a rank-based estimator will be derived. Based on its asymptotic normality property, a consistent sandwich estimator of the corresponding asymptotic covariance matrix is developed. In order to overcome the under coverage issue of the normal approximation procedure, the empirical likelihood based on the rank-based gradient function is defined, and its asymptotic distribution is established. Extensive simulation experiments under different settings of error distributions with different missing mechanisms will be considered, and the simulation results will show that the proposed empirical likelihood approach has better performance in terms of coverage probability and average length of confidence intervals for the regression parameters compared with the normal approximation approach and its least-squares counterpart. A real data example will be provided to illustrate our methods.
Relational Format
presentation
Recommended Citation
Bindele, Huybrechts, "Rank-based Estimating Equation with non-ignorable missing responses via empirical likelihood" (2024). Probability & Statistics Seminar. 9.
https://egrove.olemiss.edu/math_statistics/9
