學術報告

報告人：陳家骅 教授（University of British Columbia，雲南大學）

報告人簡介：陳家骅，卑詩大學（UBC）一等講座教授，雲南大學大數據研究院院長。2005年被加拿大統計學會授于CRM一 SSC年度獎，2014年獲加拿大統計學會最高金獎。2005年當選為美國數理統計學會(IMS)會士，2009年當選為美國統計學會(ASA)會士。陳家骅教授在統計學諸多領域都作出了重要貢獻：早期師從吳建福教授研究試驗設計，其後從事混合模型、遺傳統計學、抽樣理論、經驗似然和變量選擇方面的研究。已在國際統計學頂級雜志如Annals of Statistics, JASA, JRSSB, Biometrika等上發表論文110餘篇，其中至少52篇論文的被引次數超過10，引用次數超過100的論文有7篇。陳家骅教授是多個具有影響力的國際統計雜志的主編或者副主編，比如Canadian Journal of Statistics主編，以及Statistica Sinica，Quality Technology and Quality Management的副主編等。

題目：Small Area Quantile Estimation

摘要：Sample surveys are widely used to obtain information about totals, means, medians and other parameters of finite populations. In many applications, similar information is also desired on sub-populations such as individuals in specific geographic areas and socio-demographic groups. Often, the surveys are conducted at national or similarly high levels. The random nature of the probability sampling can result in few sampling units from many unplanned sub-populations at the design stage. Estimating parameters of these sub-populations (small areas) with satisfactory precision and evaluating their accuracy pose serious challenges to statisticians. Short of direct information, statisticians resort to pooling information across small areas via suitable model assumptions and administrative archives and census data. In this paper, we propose three estimators of small area quantiles for populations admitting a linear structure with normal error distributions or error distributions satisfying a semi-parametric density ratio model (DRM). We studies the asymptotic properties of the DRM-based method and find it root-n consistent. Extensive simulation studies are used to reveal properties of three methods under various foreseeable populations. The DRM-based is found significantly more efficient when the error distribution is skewed and has comparable efficiency with other methods in other cases.

報告時間：2016年5月16日（星期一）上午10:00-11:00

報告地點：科技樓南樓702