Bias correction for outlier estimation in time series

被引：1

作者：

Battaglia, Francesco ^{[1
]}

机构：

[1] Univ Roma La Sapienza, Dipartimento Stat Probabil & Stat Applicate, I-00100 Rome, Italy

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2006年 / 136卷 / 11期

关键词：

autoregressive process; additive outlier; innovation outlier; linear process;

D O I：

10.1016/j.jspi.2005.04.002

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：

The problem of outlier estimation in time series is addressed. The least squares estimators of additive and innovation outliers in the framework of linear stationary and non-stationary models are considered and their bias is evaluated. As a result, simple alternative nearly unbiased estimators are proposed both for the additive and the innovation outlier types. A simulation study confirms the theoretical results and suggests that the proposed estimators are effective in reducing the bias also for short series. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：3904 / 3930

页数：27

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