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

页码：185 / 219

页数：35

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