講座題目:  Sufficient Dimension Folding for Regression Mean Function

主講人: 對外經(jīng)濟貿(mào)易大學(xué)統(tǒng)計學(xué)院經(jīng)濟統(tǒng)計系 薛原(助理教授)

主講人簡介:

薛原, 2012獲美國佐治亞大學(xué)統(tǒng)計學(xué)博士學(xué)位,師從殷向榮教授.研究方向為高維數(shù)據(jù)充分降維；數(shù)據(jù)區(qū)分與判別分析；數(shù)據(jù)挖掘與變量選擇；高維數(shù)據(jù)計算等.曾獲美國東南區(qū)統(tǒng)計協(xié)會Boyd Harshbarger poster獎, 美國統(tǒng)計師協(xié)會北卡羅來納州分會AISC 2012 青年研究員獎, 佐治亞大學(xué)研究生院會議獎等各類獎項.并在統(tǒng)計學(xué)年會、統(tǒng)計跨學(xué)科國際會議、泛華統(tǒng)計統(tǒng)計協(xié)會主辦的ICSA2013等國際會議上作受邀報告.

時間：2014年5月19日下午15:00點 

地點：kaiyun開云官方網(wǎng)站犀浦校區(qū)kaiyun開云官方網(wǎng)站會議室X2511 

內(nèi)容簡介:

In this paper, we consider sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. We propose a new concept named central mean dimension folding subspace and its two local estimation methods: folded outer product of gradients estimation (folded-OPG) and folded minimum average variance estimation (folded-MAVE). We establish the asymptotic properties for folded-MAVE. A modified BIC criterion is used to determine the dimensions of the central mean dimension folding subspace. We evaluate the performances of the two local estimation methods by simulated examples and demonstrate the efficacy of folded-MAVE in finite samples. And in particular, we apply our methods to analyze a longitudinal study of primary biliary cirrhosis.