創(chuàng)源大講堂學(xué)術(shù)講座系列

講座題目: Jackknife empirical likelihood-based inferences for a low

                    income proportion

報(bào)告人：  Gengsheng Qin 教授，佐治亞州立大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院

主持人：  殷向榮教授

講座時(shí)間：2014年6月27日下午 3:00pm-4:00pm

講座地點(diǎn)：犀浦校區(qū)二號(hào)教學(xué)樓X2511

內(nèi)容簡(jiǎn)介: Low income proportion is an important index in describing the inequality of an income distribution. It has been widely used by governments in measuring social stability around the world. Established inferential methods  for this index are based on the empirical estimator of the index. It may have  poor finite sample performances when the real income data is skewed or has outliers. In this paper, we propose a smooth estimator of the low income proportion, and a smoothed jackknife empirical likelihood approach for inferences of the low income proportion. Wilks theorem is obtained for the proposed jackknife empirical likelihood ratio statistic. Various confidence intervals based on the smooth estimator are constructed. Extensive simulation studies are conducted to compare the finite sample performances of the proposed intervals with some existing intervals. Finally, the proposed methods are illustrated by a public income dataset of the professors in University System of Georgia.

主講人簡(jiǎn)介：

      Qin,Gengsheng，佐治亞州立大學(xué)教授、研究生主任，博士畢業(yè)于香港科技大學(xué)，分別在加拿大維多利亞大學(xué)以及美國印第安那大學(xué)做博士后?，F(xiàn)任國際統(tǒng)計(jì)雜志Annals of Statistics，Annals of the Institute of Statistical Mathematics，Australia and New Zealand Journal of Statistics，Biometrics，Canadian Journal of Statistics等雜志審稿人，發(fā)表學(xué)術(shù)論文四十余篇。

主辦：研究生院

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