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标题: 学术讲座 · 预告【新加坡国立大学夏应存教授】【肯塔基大学殷向荣教授】 [打印本页]

作者: 我心不变 时间: 2021-1-3 21:31
标题: 学术讲座 · 预告【新加坡国立大学夏应存教授】【肯塔基大学殷向荣教授】
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讲座安排
时间	主题	主讲人
第一场	Jackknife Approach to The Estimation of Mutual Information	夏应存
时间	2018年12月20日（星期四）下午2:00-3:00	——
地点	西南财经大学柳林校区弘远楼408会议室	——
第二场	Fourier Transform Approach for Inverse Dimension Reduction Method	殷向荣
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	——

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主题
Jackknife Approach to The Estimation of Mutual Information
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主讲人简介
主讲人：夏应存
新加坡国立大学
His research interests include high dimensional data analysis, econometric models and risk management, and statistical modelling of infectious diseases, among others.
详细简介
Prof. Yingcun Xia, received Ph. D. in Statistics from University of Hong Kong in 1999. He worked as a research associate at London school of Economics and Politics and the University of Cambridge from 2000 to 2003. He has been working in Statistics and Applied Probability at National University of Singapore since 2003 and promoted to professor in 2009.Now he is the associate editor of Annals of Statistics, Computational Statistics and Data Analysis, and Computational Statistics.
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内容摘要
Quantifying the dependence between two random variables is a fundamental issue in data analysis, and thus many measures have been proposed. Recent studies have focused on the renowned mutual information (MI) [Reshef DN, et al. (2011)]. However, “Unfortunately, reliably estimating mutual information from finite continuous data remains a significant and unresolved problem” [Kinney JB, Atwal GS (2014)]. In this paper, we examine the kernel estimation of MI and show that the bandwidths involved should be equalized. We consider a jackknife version of the kernel estimate with equalized bandwidth and allow the bandwidth to vary over an interval. We estimate the MI by the largest value among these kernel estimates and establish the associated theoretical underpinnings.
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主题
Fourier Transform Approach for Inverse Dimension Reduction Method
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主讲人简介
主讲人：殷向荣
肯塔基大学
His research interests are sufficient dimension reduction, multivariate analysis and big data analytics
详细简介
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. He has published 62 papers, including JASA, JRSSB, Biometrika and AOS.
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内容摘要
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.
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