報(bào) 告 人：王漢生 教授
報(bào)告題目：Mixture Conditional Regression with Ultrahigh Dimensional Text Data for Estimating Extralegal Factor Effects
報(bào)告時(shí)間：2024年3月14日（周四） 上午10:00
報(bào)告地點(diǎn)：靜遠(yuǎn)樓1506學(xué)術(shù)報(bào)告廳
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院、數(shù)學(xué)研究院、科學(xué)技術(shù)研究院
報(bào)告人簡介：
王漢生，1998年北京大學(xué)數(shù)學(xué)學(xué)院概率統(tǒng)計(jì)系本科畢業(yè)，2001年美國威斯康星大學(xué)麥迪遜分校統(tǒng)計(jì)系博士畢業(yè)。2003年加入光華至今，歷任副系主任（2007—2013），系主任（2013—2021）。國內(nèi)外各種專業(yè)雜志上發(fā)表文章100+篇，并合著有英文專著共1本，（合）著中文教材3本。國家杰出青年基金獲得者，全國工業(yè)統(tǒng)計(jì)學(xué)教學(xué)研究會(huì)青年統(tǒng)計(jì)學(xué)家協(xié)會(huì)創(chuàng)始會(huì)長，美國數(shù)理統(tǒng)計(jì)協(xié)會(huì)（IMS）Fellow，美國統(tǒng)計(jì)學(xué)會(huì)（ASA）Fellow，國際統(tǒng)計(jì)協(xié)會(huì)（ISI）Elected Member。先后歷任9個(gè)國際學(xué)術(shù)期刊副主編（Associate Editor / Editor）。國內(nèi)外各種專業(yè)雜志上發(fā)表文章100+篇，并合著有英文專著共1本，（合）著中文教材4本。
報(bào)告摘要:
Testing judicial impartiality is a problem of fundamental importance in empirical legal studies, for which standard regression methods have been popularly used to estimate the extralegal factor effects. However, those methods cannot handle control variables with ultrahigh dimensionality, such as those found in judgment documents recorded in text format. To solve this problem, we develop a novel mixture conditional regression (MCR) approach, assuming that the whole sample can be classified into a number of latent classes. Within each latent class, a standard linear regression model can be used to model the relationship between the response and a key feature vector, which is assumed to be of a fixed dimension. Meanwhile, ultrahigh dimensional control variables are then used to determine the latent class membership, where a na\ive Bayes type model is used to describe the relationship. Hence, the dimension of control variables is allowed to be arbitrarily high. A novel expectation-maximization algorithm is developed for model estimation. Therefore, we are able to estimate the key parameters of interest as efficiently as if the true class membership were known in advance. Simulation studies are presented to demonstrate the proposed MCR method. A real dataset of Chinese burglary offenses is analyzed for illustration purposes.