Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Smoothed rank correlation of the linear transformation regression model

Language

English

Abstract

The maximum rank correlation (MRC) approach is the most common method used in the literature to estimate the regression coefficients in the semiparametric linear transformation regression model. However, the objective function Gn(β) in the MRC approach is not continuous. The optimization of Gn(β) requires an extensive search for which the computational cost grows in the order of nd, where d is the dimension of X. Given the lack of smoothing, issues related to variable selection, the variance estimate and other inferences by MRC are not well developed in the model. In this paper, we combine the concept underlying the penalized method, rank correlation and smoothing technique and propose a nonconcave penalized smoothed rank correlation method to select variables and estimate parameters for the semiparametric linear transformation model. The proposed estimator is computationally simple, n1 2-consistent and asymptotically normal. A sandwich formula is proposed to estimate the variances of the proposed estimates. We also illustrate the usefulness of the methodology with real data from a body fat prediction study. © 2012 Elsevier B.V. All rights reserved.

Keywords

MRC, Semiparametric transformation model, Variable selection, Variance estimation

Publication Date

2013

Source Publication Title

Computational Statistics & Data Analysis

Volume

57

Issue

1

Start Page

615

End Page

630

Publisher

Elsevier

DOI

10.1016/j.csda.2012.07.012

Link to Publisher's Edition

http://dx.doi.org/10.1016/j.csda.2012.07.012

ISSN (print)

01679473

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