Robust estimation and selection for single-index regression model
Document Type
Lecture
Publication Date
10-12-2022
Abstract
In this talk, we will consider a single-index regression model, from which we will discuss a robust estimation procedure for the model parameters and an efficient variable selection of relevant predictors. The proposed approach known as the penalized generalized signed-rank procedure will be introduced. Asymptotic properties of the resulting estimators will be discussed under mild regularity conditions. Extensive Monte Carlo simulation experiments will be carried out to study the finite sample performance of the proposed approach. The simulation results will demonstrate that the proposed approach dominates many of the existing ones in terms of robustness in estimation and efficiency of variable selection. Finally, a real data example will be discussed to illustrate the method.
Relational Format
presentation
Recommended Citation
Bindele, Huybrechts, "Robust estimation and selection for single-index regression model" (2022). Probability & Statistics Seminar. 26.
https://egrove.olemiss.edu/math_statistics/26
