概率统计学术报告(3月30日)

报告人：Professor Chor-yiu (CY) SIN 

National Tsing Hua University, Taiwan 

题目：Asymptotic risk of order selection in high-dimensional autoregressive models 

时间：2017年3月30日（周四）下午3:00-4:00 

地点：工商管理楼2楼报告厅 

摘要：Most order selection methods in autoregressive (AR) models are devised for processes of integrated of order 0 (I(d) processes, d = 0). We consider in this paper an I(d) AR process,  is an unknown integer and the lag order is finite. The number of lags considered, Pn(d), may be finite; and in view of the flourishing literature on high-dimensional models, Pn(d) may also go to infinity, when the sample size, n, does. This paper first shows that Akaike's information criterion (AIC) is asymptotically inefficient (in terms of prediction) in finite-order AR processes, while the Bayesian information criterion (BIC) or the Hannan Quninn information criterion (HQIC) is asymptotically efficient. In other words, our results give some warnings on inappropriate choices of the penalty term in an information criterion, should the true lag order is finite. At the same time, we extend the asymptotic risk of order selection in AR models in fourfold: (i) a general I(d) process; (ii) the same realization of the data; (iii) an information criterion that is more general than AIC; and (iv)  or.

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联系人：张荣茂 rmzhang@zju.edu.cn 

浙江大学统计研究所