報(bào)告題目： Model Averaging in Linear Measurement Error Models
主講人：張新雨博士（中國科學(xué)院）
時(shí)間：2015年7月3日16:10-17:10
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：We develop model averaging estimation in the linear regression model where covariates are subject to measurement errors. The absence of the true covariates in this framework makes the calculation of the standard residual based loss function impossible. We take advantage of the explicit form of the parameter estimators and construct a new criterion. It has the property that it is asymptotically equivalent to the unknown model average estimator minimizing the loss function. When the true model is not included in the set of candidate models, the method achieves optimality in terms of minimizing the relative loss, while when the true model is included, the method estimates the model parameter with root-n rate. Numerical analysis in comparison with existing BIC and AIC model selection and model averaging methods strongly favors our new model averaging method.
張新雨博士簡介：統(tǒng)計(jì)學(xué)博士（中科院），數(shù)學(xué)和系統(tǒng)科學(xué)研究所副研究員，主要研究領(lǐng)域?yàn)槟Ｐ推骄?選擇，組合預(yù)測以及混合效應(yīng)模型。在Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of Econometrics等頂級統(tǒng)計(jì)學(xué)和計(jì)量經(jīng)濟(jì)學(xué)期刊發(fā)表過多篇論文，是中科院優(yōu)秀博士學(xué)位論文獎（2011）獲得者。
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