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肯塔基大学殷向荣教授:Fourier Transform Approach for Inverse Dimension Reduction Method

光华讲坛——社会名流与企业家论坛第 5187期

主题:Fourier Transform Approach for Inverse Dimension Reduction Method

主讲人:肯塔基大学殷向荣教授

主持人:统计学院 林华珍教授

时间:2018年12月20日(星期四)下午3:00-4:00

地点:西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心 统计学院 科研处

主讲人简介:

Xiangrong Yin is Professor of Statistics at the University of Kentucky since 2014. He obtained his PhD degree in 2000 at the University of Minnesota. He was assistant professor, associate professor and professor at the University of Georgia (2000-2014). His paper with his adviser R. D. Cook won the 2001 The Inaugural Editor's Award for the best article published in the Australian and New Zealand Journal of Statistics. His paper with his student Yuan Xue won The Journal of Nonparametric Statistics Best Student Paper Prize 2015. He was an associate editor for Statistica Sinica (2014-2017) and Statistics and Probability Letters (2010-2014. He has been an associate editor since 2010 for Journal of Nonparametric Statistics. He has guided twelve PhD students and his research are partially supported by NSF grants, his research interests are sufficient dimension reduction, multivariate analysis and big data analytics. He has published 62 papers, including JASA, JRSSB, Biometrika and AOS.

主要内容:

Estimating an inverse regression space is especially important in sufficient dimension reduction. However, it typically requires a tuning parameter, such as the number of slices in a slicing method or bandwidth selection in a kernel estimation approach. Such a requirement not only affects the accuracy of estimates in a finite sample, but also increases difficulties for multivariate models. In this paper, we use a Fourier transform approach to avoid such difficulties and incorporate multivariate models. We further develop a Fourier transform approach to deal with variable selection, categorical predictor variables, and large p, small n data. To test the dimension, asymptotic results are obtained. Simulation studies and data analysis show the efficacy of our proposed methods.

光华讲坛——社会名流与企业家论坛第 期

主题:Fourier Transform Approach for Inverse Dimension Reduction Method

主讲人:肯塔基大学殷向荣教授

主持人:统计学院 林华珍教授

时间:2018年12月20日(星期四)下午3:00-4:00

地点:西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心 统计学院 科研处

主讲人简介:

Xiangrong Yin is Professor of Statistics at the University of Kentucky since 2014. He obtained his PhD degree in 2000 at the University of Minnesota. He was assistant professor, associate professor and professor at the University of Georgia (2000-2014). His paper with his adviser R. D. Cook won the 2001 The Inaugural Editor's Award for the best article published in the Australian and New Zealand Journal of Statistics. His paper with his student Yuan Xue won The Journal of Nonparametric Statistics Best Student Paper Prize 2015. He was an associate editor for Statistica Sinica (2014-2017) and Statistics and Probability Letters (2010-2014. He has been an associate editor since 2010 for Journal of Nonparametric Statistics. He has guided twelve PhD students and his research are partially supported by NSF grants, his research interests are sufficient dimension reduction, multivariate analysis and big data analytics. He has published 62 papers, including JASA, JRSSB, Biometrika and AOS.

主要内容:

Estimating an inverse regression space is especially important in sufficient dimension reduction. However, it typically requires a tuning parameter, such as the number of slices in a slicing method or bandwidth selection in a kernel estimation approach. Such a requirement not only affects the accuracy of estimates in a finite sample, but also increases difficulties for multivariate models. In this paper, we use a Fourier transform approach to avoid such difficulties and incorporate multivariate models. We further develop a Fourier transform approach to deal with variable selection, categorical predictor variables, and large p, small n data. To test the dimension, asymptotic results are obtained. Simulation studies and data analysis show the efficacy of our proposed methods.