應學院邀請，西安交通大學朱學虎教授将在線作學術報告。
報告題目：Specification testing of regression models with mixed discrete and continuous predictors
報告摘要：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.
報告時間：2022年11月13日10:00
報告地點：騰訊會議号(371267370)
邀 請 人：田玉柱副教授
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報告人簡介
朱學虎，博士，西安交通大學教授，博士生導師，于2015年在香港浸會大學和山東大學獲得雙博士學位，主要從事統計學習、金融統計、高維數據分析、應用統計等方面的基礎理論與應用研究。截止目前，發表SCI或者SSCI論文20餘篇，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等雜志；先後主持國家自然科學基金青年項目、國家社會科學基金項目（一般項目）、中國博士後特别資助項目等項目；曾獲2022陝西省高校青年傑出人才支持計劃、2021年仲英青年學者、2018年陝西省數學會青年教師優秀論文一等獎等榮譽。
