Outlier Detection in Adaptive Functional-Coefficient Autoregressive Models Based on Extreme Value Theory
Joint Authors
Chen, Ping
Dong, Ling
Chen, Wanyi
Lin, Jin-Guan
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-04-15
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper proposes several test statistics to detect additive or innovative outliers in adaptive functional-coefficient autoregressive (AFAR) models based on extreme value theory and likelihood ratio tests.
All the test statistics follow a tractable asymptotic Gumbel distribution.
Also, we propose an asymptotic critical value on a fixed significance level and obtain an asymptotic p-value for testing, which is used to detect outliers in time series.
Simulation studies indicate that the extreme value method for detecting outliers in AFAR models is effective both for AO and IO, for a lone outlier and multiple outliers, and for separate outliers and outlier patches.
Furthermore, it is shown that our procedure can reduce possible effects of masking and swamping.
American Psychological Association (APA)
Chen, Ping& Dong, Ling& Chen, Wanyi& Lin, Jin-Guan. 2013. Outlier Detection in Adaptive Functional-Coefficient Autoregressive Models Based on Extreme Value Theory. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1011127
Modern Language Association (MLA)
Chen, Ping…[et al.]. Outlier Detection in Adaptive Functional-Coefficient Autoregressive Models Based on Extreme Value Theory. Mathematical Problems in Engineering No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1011127
American Medical Association (AMA)
Chen, Ping& Dong, Ling& Chen, Wanyi& Lin, Jin-Guan. Outlier Detection in Adaptive Functional-Coefficient Autoregressive Models Based on Extreme Value Theory. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1011127
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1011127