Statistical Inference on Transformation Models: a Self-induced Smoothing Approach
Abstract
This paper deals with a general class of transformation models that contains many important semiparametric regression models as special cases. It develops a self-induced smoothing for the maximum rank correlation estimator, resulting in simultaneous point and variance estimation. The self-induced smoothing does not require bandwidth selection, yet provides the right amount of smoothness so that the estimator is asymptotically normal with mean zero (unbiased) and variance-covariance matrix consistently estimated by the usual sandwich-type estimator. An iterative algorithm is given for the variance estimation and shown to numerically converge to a consistent limiting variance estimator. The approach is applied to a data set involving survival times of primary biliary cirrhosis patients. Simulations results are reported, showing that the new method performs well under a variety of scenarios.
Cite
@article{arxiv.1302.6651,
title = {Statistical Inference on Transformation Models: a Self-induced Smoothing Approach},
author = {Junyi Zhang and Zhezhen Jin and Yongzhao Shao and Zhiliang Ying},
journal= {arXiv preprint arXiv:1302.6651},
year = {2013}
}