LOCAL ASYMPTOTIC NORMALITY FOR AUTOREGRESSION WITH INFINITE-ORDER

被引：16
|
作者：
KREISS, JP [1 ]
机构：
[1] UNIV HAMBURG,INST MATH STOCHASTIK,W-2000 HAMBURG 13,GERMANY
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 1990年 / 26卷 / 02期
关键词：
adaptation; Autoregressive process; local asymptotically minimax; local asymptotically normal; parameter estimation; simulation;
D O I：
10.1016/0378-3758(90)90126-F
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We consider families of real valued random variables which form a stationary autoregressive process with infinite order. Using a certain local parametrization we are able to establish that such models are locally asymptotically normal. This property admits (using well-known results) a characterization and a construction of local asymptotically minimax estimates for a finite dimensional subparameter of the model. Finally we will show that even adaptive estimation is possible. © 1990.
引用
收藏
页码：185 / 219
页数：35
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