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主讲人 |
李长城 |
简介 |
<p>We propose a model-free variable selection approach, namely constrained kernel regression. Instead of relying on model-based loss functions, the proposed approach is developed based on conditional independence relationship measured by conditional distance covariance/correlation. The conditional distance covariance/correlation is further approximated using the kernel density estimation method. The regression coefficient vector is then defined to be the vector satisfying the approximated conditional independence constraints. We prove that the proposed approach can consistently identify the true important predictor set under high-dimensional model-free settings with appropriate tuning parameters. The advantage of the proposed procedure is further shown by various numerical studies. More specifically, the proposed model-free procedure surpasses the existing model-based methods in the presence of model misspecification while outperforms or at least equates to the existing ones with correctly specified models.</p> |
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主讲人简介 |
<p>李长城，大连理工大学数学科学学院教授、博士生导师，研究兴趣主要为高维统计、高维因果推断及因果图学习。高维统计及因果推断是目前统计领域的热点，在智能决策、生物医学等诸多领域里都有非常重要的应用。在高维统计的理论、应用以及计算方面进行了一系列创新性的研究，在国际顶尖学术期刊Journal of the American Statistical Association、Annals of Statistics、Journal of Econometrics、Annals of Applied Statistics等发表文章多篇。入选小米青年学者、辽宁省及国家级青年人才项目。</p> |
期数 |
高级计量经济学与统计学系列讲座2025年春季学期第六讲（总184讲） |
