报告人： Yuhong Yang, University of Minnesota
时间：2021-07-19 10:00-11:30
地点：Guanghua Building 2, Room 217
Abstract:
Assessing a binary regression model based on ungrouped data is a commonly encountered but very challenging problem. Although tests, such as Hosmer–Lemeshow test, have been devised and widely used in applications, they often have low power in detecting lack of fit and not much theoretical justification has been made on when they can work well. In this talk, we will present two new approaches to address the problem.
The first approach is based on a cross-validation (CV) voting system: The model under examination is compared to a nonparametric method and a lack of fit is declared when the nonparametric method wins the competition. Under some mild conditions, the CV comparison leads to both type I and II error probabilities converging to zero.
The second approach intends to control the probability of type I error while enhancing the power. It is also applicable to assess a general classification learning method (e.g., neural network). The new methodology, named binary regression adaptive grouping goodness-of- fit test (BAGofT), is a two-stage solution where the first stage adaptively selects candidate partitions using "training" data, and the second stage performs tests based on "test" data. A proper data splitting ensures that the test has desirable size and power properties. From our experimental results, BAGofT performs much better than the Hosmer-Lemeshow and related tests in many situations.
The work is joint with Jie Ding, Chunling Lu and Jiawei Zhang.
About the Speaker:
Yuhong Yang received his Ph.D from Yale in statistics in 1996. He then joined Department of Statistics at Iowa State University and moved to the University of Minnesota in 2004. His research interests include model selection, multi-armed bandit problems, forecasting, high-dimensional data analysis, and machine learning. He has published in top journals in several fields, including Annals of Statistics, JASA, JRSSB, Biometrika, IEEE Transaction on Information Theory, Journal of Econometrics, Proceedings of AMS, Journal of Machine Leaning Research, and International Journal of Forecasting. He is a fellow of Institute of Mathematical Statistics and was a recipient of the US NSF CAREER Award.
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