Title
Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO
Document Type
Article
Publication Date
2-2021
Abstract
In this paper, a semi-parametric regression model with an adaptive LASSO penalty imposed on both the linear and the nonlinear components of the mode is considered. The model is rewritten so that a signed-rank technique can be used for estimation. The nonlinear part consists of a covariate that enters the model nonlinearly via an unknown function that is estimated using B-splines. The author shows that the resulting estimator is consistent under heavy-tailed distributions and asymptotic normality results are given. Monte Carlo simulations as well as practical applications are studied to assess the validity of the proposed estimation method.
DOI
10.1007/s11424-020-9097-9
Publisher
Springer
Repository Citation
Kwessi, E. (2021). Double penalized semi-parametric signed-rank regression with adaptive LASSO. Journal of Systems Science and Complexity, 34(1), 381-401. doi: 10.1007/s11424-020-9097-9
Publication Information
Journal of Systems Science and Complexity
