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              9月27日北京師范大學朱力行教授來我院線上講座預告
              ( 來源:   發布日期:2020-09-21 閱讀:次)

              講座主題:Integrated conditional moment test and beyond: when the number of covariates is divergent
              主講人: 朱力行教授 北京師范大學
              講座時間:2020/9/27 16:00-18:00
              參與方式:點擊鏈接入會,或添加至會議列表:
              https://meeting.tencent.com/s/USymBxRBFyLk
              會議 ID:362 878 228
              會議直播: https://meeting.tencent.com/l/nAHdJ64IJHQJ
              主講人簡介:
              朱力行,1990年在中國科學院獲得理學博士,1993年在中國科學院應用數學所評為研究員/博士導師,F在是北京師范大學統計學院教授。1998年獲得德國洪堡研究獎,是自然科學,工程,醫學領域中,大陸,香港,臺灣,澳門第一位獲獎者,迄今為止,還是亞洲統計學界唯一獲獎者。2003年,2007年和2016年分別當選為美國數理統計研究院fellow,美國統計協會fellow和美國科學促進會fellow. 2013年獨立獲得中國國家自然科學獎二等獎。1997年獲得杰出青年基金資助,1999年入選中科院百人計劃。2004年獲選為長江講座教授。

              講座摘要:
              The classic integrated conditional moment (ICM) test is a proven promising method for testing model misspecification for fixed dimension paradigms. However, in diverging dimension scenarios, our study in this paper shows the failures of this test and the related wild bootstrap approximation to maintain the significance level and keep reasonable powers because of completely different limiting properties from those in fixed dimension cases. To extend the ICM test to handle the testing problem with diverging number of covariates, we investigate three issues in inference in this paper. First, under both the null and alternative hypothesis, we study the consistency and asymptotically linear representation of the least squares estimator of the parameter at the fastest rate of divergence in the literature for nonlinear models. Second, we propose a projected adaptive-to-model version of the integrated conditional moment test. We study the asymptotic properties of the new test under both the null and alternative hypothesis to examine its ability of significance level maintenance and its sensitivity to the global and local alternatives that are distinct from the null at the fastest possible rate in hypothesis testing. Third, we derive the consistency of the wild bootstrap approximation for the null distribution such that its availability for approximating the null distribution of the test in the diverging dimension setting. The numerical studies show that the new test can very much enhance the performance of the original ICM test in high-dimensional cases. We also apply the test to a real data set for illustrations.




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