主题：
REQML estimator for mixed regressive, spatial autoregressive model and its small sample bias
主讲人：
喻达磊博士
时间:
2014年10月24日（周五）9:20
地点
北院卓远楼305
主办单位：
统计与数学学院
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.
主讲人喻达磊简介：
香港城市大学统计学博士，副教授。研究领域为随机效应模型、混合模型和空间计量经济学模型的统计推断。已在Stat. Med.、J. Multivariate Anal.等国际统计期刊发表论文多篇。
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