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主題：
REQML estimator for mixed regressive, spatial autoregressive model and its small sample bias

主講人：
喻達(dá)磊博士

時(shí)間:
2014年10月24日（周五）9:20

地點(diǎn)
北院卓遠(yuǎn)樓305

主辦單位：
統(tǒng)計(jì)與數(shù)學(xué)學(xué)院

摘要：
Mixed regressive, spatial autoregressive (MRSAR) model is widely used in geostatistics, spatial econometrics, regional science and urban economics. Under flexible distributional assumptions, the restricted quasi-maximum likelihood (REQML) estimator for mixed regressive, spatial autoregressive model is studied in this study. The proposed estimation method accommodates the extra uncertainty introduced by the unknown regression coefficients. Moreover, the explicit expressions of theoretical/feasible second-order-bias of the RQEML estimator are derived and the difference between them is investigated. The feasible second-order-bias corrected REQML estimator is then designed accordingly for small sample setting. Extensive simulation studies are conducted under both normal and non-normal situations, showing that the quasi-maximum likelihood (QML) estimator suffers from large bias when the sample size is relatively small and such bias can be effectively eliminated by the proposed REQML estimation method. The use of the method is then demonstrated in the analysis of the Neighborhood Crimes Data.

主講人喻達(dá)磊簡(jiǎn)介：
香港城市大學(xué)統(tǒng)計(jì)學(xué)博士，副教授,。研究領(lǐng)域?yàn)殡S機(jī)效應(yīng)模型、混合模型和空間計(jì)量經(jīng)濟(jì)學(xué)模型的統(tǒng)計(jì)推斷。已在Stat. Med.,、J. Multivariate Anal.等國(guó)際統(tǒng)計(jì)期刊發(fā)表論文多篇。