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數(shù)學(xué)與統(tǒng)計(jì)學(xué)院"21世紀(jì)學(xué)科前沿"系列學(xué)術(shù)報(bào)告預(yù)告
Second-order Least Squares Method for High-dimensional Variable Selection
作者： ?? 來源：數(shù)學(xué)學(xué)院?? 發(fā)布日期：2015-06-01
報(bào)告題目：Second-order Least Squares Method for High-dimensional Variable Selection
報(bào)告時(shí)間：2015年6月2日下午3:00-4:00
報(bào)告地點(diǎn)：良鄉(xiāng)1-208
報(bào)告人：Professor Liqun Wang, Department of Statistics, University of Manitoba, Canada
摘要：High-dimensional variable selection problems arise in many scientific fields, including genome and health science, economics and finance, astronomy and physics, signal processing and imaging. In statistics, various regularization methods have been studied based on either likelihood or least squares principles. In this talk, I will propose a regularized second order least squares method for variable selection in linear or nonlinear regression models. This method is based the first two conditional moments of the response variable given on the predictor variables. It is asymptotically more efficient than the ordinary least squares method when the regression error has nonzero third moment. Consequently the new method is more robust against asymmetric error distributions. I will demonstrate the effectiveness of this method through Monte Carlo simulation studies. A real data application will be presented to further illustrate the method.
報(bào)告時(shí)間：2015年6月2日下午3:00-4:00
報(bào)告地點(diǎn)：良鄉(xiāng)1-208
報(bào)告人：Professor Liqun Wang, Department of Statistics, University of Manitoba, Canada
摘要：High-dimensional variable selection problems arise in many scientific fields, including genome and health science, economics and finance, astronomy and physics, signal processing and imaging. In statistics, various regularization methods have been studied based on either likelihood or least squares principles. In this talk, I will propose a regularized second order least squares method for variable selection in linear or nonlinear regression models. This method is based the first two conditional moments of the response variable given on the predictor variables. It is asymptotically more efficient than the ordinary least squares method when the regression error has nonzero third moment. Consequently the new method is more robust against asymmetric error distributions. I will demonstrate the effectiveness of this method through Monte Carlo simulation studies. A real data application will be presented to further illustrate the method.