Title

Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO

Document Type

Article

Publication Date

2020

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.

Document Object Identifier (DOI)

10.1007/s11424-020-9097-9

Publisher

Springer

Publication Information

Journal of Systems Science and Complexity

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