Pengfei Li | Maximum pairwise-rank-likelihood-based inference for the semiparametric transformation model

时间:2018-07-14浏览:101设置

时间: 2018年08月09日上午9:00-10:00

地点: 闵行校区法商南楼135室

报告人: Pengfei Li, Associate Professor, University of Waterloo

题目: Maximum pairwise-rank-likelihood-based inference for the semiparametric transformation model

摘要:

In this talk, we study the linear transformation model in the most general setup. This model includes many important and popular models in statistics and econometrics as special cases. Although it has been studied for many years, the methods in the literature either are based on kernel-smoothing techniques or make use of only the ranks of the responses in the estimation of the parametric components. The former approach needs a tuning parameter, which is not easily optimally specified in practice; and the latter approach may be {less accurate and computationally expensive}. In this paper, we propose a pairwise rank likelihood method. Our method estimates all the unknown parameters in the linear transformation model, and we establish theoretically the convergence rate of our proposed estimators. Via extensive numerical studies, we demonstrate that our method is appealing in that the estimators are not only robust to the distribution of the random errors but also in many cases more accurate than those of the existing methods. This talk is based on a joint work with Drs Tao Yu, Baojiang Chen, and Jing Qin.


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