报告题目：Efficient inference for autoregression in the presence of trend

报告人： 杨立坚 教授

(美国Michigan州立大学教授）

摘要：Yule-Walker estimators are constructed for autoregressive coefficients based on the observed time series that contains an unknown trend function and an autoregressive error term. The trend function is estimated by means of B-spline or local polynomial smoothing and then subtracted from the observations. The Yule-Walker estimators are then obtained from the residual sequence, which enjoy oracle efficiency, i.e., they are asymptotically as efficient as if the true trend function were known. The performance of the estimator is illustrated by simulation studies and real data analysis.

时间：2012年4月23日（周一）上午9:00开始

地点：数学系致远楼 102会议室