应学院邀请，西安交通大学朱学虎教授将在线作学术报告。
报告题目：Specification testing of regression models with mixed discrete and continuous predictors
报告摘要：In this paper, we propose a nonparametric projection-based adaptive-to-model specification test for regressions with both discrete and continuous predictors. The test statistic is asymptotically normal under the null hypothesis and omnibus against alternative hypotheses. The test behaves like a locally smoothing test as if the number of continuous predictors was one, and can detect the local alternative hypotheses distinct from the null hypothesis at the rate that can be achieved by existing locally smoothing tests for regressions with only one continuous predictor. Because of the model adaptation property, the test can fully utilize the model structure under the null hypothesis such that the dimensionality problem can be very much alleviated. As a by-product in the test construction, a discretization-expectation ordinary least squares estimation approach for partial central subspace in sufficient dimension reduction is developed. We suggest a residual-based wild bootstrap method to give an approximation by fully utilizing the null model and thus closer to the limiting null distribution than existing bootstrap approximations. We conduct simulation studies to compare it with existing tests and two real data examples for illustration.
报告时间：2022年11月13日10:00
报告地点：腾讯会议号(371267370)
邀 请 人：田玉柱副教授
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报告人简介
朱学虎，博士，西安交通大学教授，博士生导师，于2015年在香港浸会大学和山东大学获得双博士学位，主要从事统计学习、金融统计、高维数据分析、应用统计等方面的基础理论与应用研究。截止目前，发表SCI或者SSCI论文20余篇，其中包括Journal of the American Statistical Association、Journal of Business & Economic Statistics、Statistics Sinica、Scandinavian Journal of Statistics、Statistics and Computing、IEEE Transactions on Geoscience and Remote Sensing等杂志；先后主持国家自然科学基金青年项目、国家社会科学基金项目（一般项目）、中国博士后特别资助项目等项目；曾获2022陕西省高校青年杰出人才支持计划、2021年仲英青年学者、2018年陕西省数学会青年教师优秀论文一等奖等荣誉。
