Asymptotic properties of BMM-estimator in bidimensional autoregressive processes

被引：0

作者：

Britos, Grisel M. ^{[1
]}

Ojeda, Silvia M. ^{[2
]}

Rodriguez Astrain, Laura A. ^{[3
]}

Bustos, Oscar H. ^{[2
]}

机构：

[1] Univ Nacl Cordoba CONAE, Ctr Espacial Teofilo Tabanera, Inst Gulich, CONICET, Ruta 45 Km 8, Cordoba, Argentina

[2] Univ Nacl Cordoba, FaMAF CIEM, CONICET, Medina Allende S-N,Ciudad Univ, RA-5000 Cordoba, Argentina

[3] Univ Nacl Cordoba, FaMAF, Medina Allende S-N,Ciudad Univ, RA-5000 Cordoba, Argentina

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2020年 / 209卷

关键词：

AR-2D models; Robust estimators; Consistency; Asymptotic normality; Image processing; ROBUST ESTIMATION; ALGORITHM; MODEL;

D O I：

10.1016/j.jspi.2020.04.001

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 work, we present the BMM 2D estimator, a robust estimator for the parameters of the bidimensional autoregressive model (AR-2D model). The new estimator is a two-dimensional extension of the BMM estimator for the parameters of the autoregressive models used in time series analysis. We demonstrate that the BMM 2D estimator is consistent and asymptotically normal, which provides a valuable tool to carry out inferential studies about the parameters of the AR-2D model. Also, we show the performance of the BMM 2D estimator compared with the least-squares estimator and illustrate it in the context of image restoration problems. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：208 / 228

页数：21

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