報(bào)告題目：General matching quantiles M-estimation

報(bào)告摘要：In this talk, we first describe the background/motivation of matching quantiles estimation (MQE). After it, we briefly introduce M-estimation which is a robust alternative to the ordinary least-squares.  Then we present a matching quantiles M-estimation (MQME) method. Since the number of variables may be large in many real problems, a `sparse' representation is highly desirable. We thus integrate the proposed MQME with adaptive Lasso for selecting informative variables. In order to compute MQME, we develop an iterative algorithm. Moreover, we show that the (sparse) matching quantiles M-estimator possesses the property of asymptotic consistency. Our simulations demonstrate the efficient finite-sample performance of the proposed method. We end this talk by presenting an illustrative real case study.

報(bào)告時(shí)間：2023年10月25日，星期三，下午15:30---16:30

報(bào)告地點(diǎn)：kaiyun開云官方網(wǎng)站犀浦校區(qū)，3號(hào)教學(xué)樓30429會(huì)議室

報(bào)告人簡(jiǎn)介：吳月華，加拿大約克大學(xué)數(shù)學(xué)與統(tǒng)計(jì)系教授。1989年獲得美國(guó)匹茲堡大學(xué)統(tǒng)計(jì)學(xué)博士學(xué)位，師從世界著名統(tǒng)計(jì)學(xué)家C. R. Rao。研究領(lǐng)域非常廣泛，包括空間統(tǒng)計(jì)、M-估計(jì)、模型選擇、變點(diǎn)檢測(cè)、非參數(shù)估計(jì)、金融統(tǒng)計(jì)等，以及在環(huán)境科學(xué)、信息科學(xué)、計(jì)量經(jīng)濟(jì)學(xué)、生物醫(yī)學(xué)等領(lǐng)域中的應(yīng)用，目前是國(guó)際統(tǒng)計(jì)學(xué)會(huì)的當(dāng)選會(huì)員。在Proceeding of National Academy Science, USA (美國(guó)國(guó)家科學(xué)院院刊), Biometrika, Statistica Sinica, Computational Statistics & Data Analysis，Journal of Multivariate Analysis等期刊發(fā)表學(xué)術(shù)論文100多篇。承擔(dān)加拿大國(guó)家自然科學(xué)基金、加拿大環(huán)境署等多項(xiàng)科研項(xiàng)目。