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              11月6日加拿大滑鐵盧大學教授Dr. Pengfei Li來我院線上講座預告
              ( 來源:   發布日期:2020-11-01 閱讀:次)

              講座主題:Maximum pairwise-rank-likelihood-based inference for the semiparametric transformation model
              主講人:Dr. Pengfei Li 加拿大滑鐵盧大學
              講座時間:2020年11月6日 10:30-12:30
              參與方式:點擊鏈接入會:
              https://meeting.tencent.com/s/iYZJBNkuxWHP

              會議 ID: 476 715 760
              會議直播: 本場報告將通過騰訊會議舉辦,https://meeting.tencent.com/s/iYZJBNkuxWHP
              主講人簡介:

              Dr. Pengfei Li completed his Ph.D in Dec. 2007 from University of Waterloo and postdoctoral studies from University of British Columbia in 2008. He is Professor at University of Waterloo since 2019. Dr. Li has published around 60 papers in refereed journals or books. Fifteen of them have appeared in top statistical journals such as Annals of Statistics, Biometrika, Journal of the American Statistical Association, and Journal of the Royal Statistical Society: Series B. Dr. Li has served as the associate chair for Undergraduate Studies for two years at the University of Waterloo. He has received the Faculty of Mathematics Award for Distinction in Teaching in 2017 and Faculty of Mathematics Golden Jubilee Research Excellence Award in 2020.  Dr. Li is currently serving as associate editor of The Canadian Journal of Statistics and Metrika.

              講座摘要:

              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 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 is computationally expensive and may not make full use of the information in the data. In this talk, we propose two methods: a pairwise rank likelihood method and a score-function-based method based on this pairwise rank likelihood. We also explore the theoretical properties of the 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 lead to mean square errors that are in many cases comparable to or smaller than those of existing methods.


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