报告题目：Nonnested model selection based on empirical likelihood

报告人：蒋建成

报告摘要：

In this talk we propose an empirical likelihood ratio (ELR) test for comparing any two supervised learning models, where the competing models may be nested, nonnested, overlapping, misspecified, or correctly specified.  It compares the prediction losses of models based on the cross-validation. We develop its asymptotic distributions for comparing two nonparametric learning models under a general framework with convex loss functions. However, the prediction losses from the cross-validation involve repeatedly fitting the 20
models with one observation left out, which is a heavy computational burden. We introduce an easy-to-implement ELR test which requires fitting the models only once and shares the same asymptotics as the original one. The proposed tests are applied to compare additive models with varying-coefficient models. Furthermore, a scalable distributed ELR test is proposed for testing the importance of a group of variables in possibly misspecified additive models with massive data. Simulations show that the proposed tests work 25 well and have favorable finite sample performance over some existing approaches. The methodology is validated on an empirical application.

报告时间: 12月2日（周五）上午9:00—10:00

报告地点：腾讯会议 ID: 164-639-627

会议链接 ：https://meeting.tencent.com/dm/irjDr8l3wL8h

报告人简介：蒋建成教授， University of North Carolina at Charlotte数学与统计系教授。主要从事生物统计、金融计量经济学、非参数统计、数据科学等方面的研究，在Annals of Statistics, Biometrika, Journal of American Statistical Association, Journal of the Royal Statistical Society 等国际著名统计期刊发表论文50余篇,担任Statistica Sinica 等杂志副主编。