Adaptive R-estimation in autoregressions

被引：1

作者：

Allal, J ^{[1
]}

Faizi, M

Kaaouachi, A

机构：

[1] Univ Mohamed Ier, Fac Sci, Dept Math, Oujda 60000, Morocco

[2] Univ Mohamed Ier, EST, ENSA, Dept Informat, Oujda, Morocco

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2004年 / 33卷 / 02期

关键词：

adaptive estimation; asymptotic linearity; autoregression models; LAM estimator; LAN property;

D O I：

10.1081/STA-120028380

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：

In this paper we explore the local asymptotic normality property with a ranked residual central sequence in autoregression to give locally asymptotically minimax estimators of the autoregressive parameter. Then, we use the kernel estimator method described by Koul and Schick [Koul, H. L., Schick, A. (1997). Efficient estimation in non-linear autoregressive time series models. Bernoulli 3:247-277] to construct adaptive estimators. A simulation experiment is carried out to illustrate the performance of the proposed adaptive estimators.

引用

页码：381 / 395

页数：15

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