学术报告

报告人：陈家骅 教授（University of British Columbia，云南大学）

报告人简介：陈家骅，卑诗大学（UBC）一等讲座教授，云南大学大数据研究院院长。2005年被加拿大统计学会授于CRM一 SSC年度奖，2014年获加拿大统计学会最高金奖。2005年当选为美国数理统计学会(IMS)会士，2009年当选为美国统计学会(ASA)会士。陈家骅教授在统计学诸多领域都作出了重要贡献：早期师从吴建福教授研究试验设计，其后从事混合模型、遗传统计学、抽样理论、经验似然和变量选择方面的研究。已在国际统计学顶级杂志如Annals of Statistics, JASA, JRSSB, Biometrika等上发表论文110余篇，其中至少52篇论文的被引次数超过10，引用次数超过100的论文有7篇。陈家骅教授是多个具有影响力的国际统计杂志的主编或者副主编，比如Canadian Journal of Statistics主编，以及Statistica Sinica，Quality Technology and Quality Management的副主编等。

题目：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.

报告时间：2016年5月16日（星期一）上午10:00-11:00

报告地点：科技楼南楼702