Smoothed rank correlation of the linear transformation regression model
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 G n ( ? ) in the MRC approach is not continuous. The optimization of G n ( ? ) requires an extensive search for which the computational cost grows in the order of nd n d , where d 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? n 1 / 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.
mrc variable selection variance estimation semiparametric transformation model