| 主讲人 |
Tao Yu |
简介 |
<p>In this paper, 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 and extend it to a score-function-based method. Our methods estimate all the unknown parameters in the linear transformation model, and we explore the theoretical properties of our proposed estimators. Via extensive numerical studies, we demonstrate that our methods are 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.<br />
<br />
Joint work with Pengfei Li, Baojiang Chen, Ao Yuan and Jing Qin</p> |
