Robust AIC with High Breakdown Scale Estimate
Author
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-08
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Akaike Information Criterion (AIC) based on least squares (LS) regression minimizes the sum of the squared residuals; LS is sensitive to outlier observations.
Alternative criterion, which is less sensitive to outlying observation, has been proposed; examples are robust AIC (RAIC), robust Mallows Cp (RCp), and robust Bayesian information criterion (RBIC).
In this paper, we propose a robust AIC by replacing the scale estimate with a high breakdown point estimate of scale.
The robustness of the proposed methods is studied through its influence function.
We show that, the proposed robust AIC is effective in selecting accurate models in the presence of outliers and high leverage points, through simulated and real data examples.
American Psychological Association (APA)
Saleh, Shokrya. 2014. Robust AIC with High Breakdown Scale Estimate. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1039650
Modern Language Association (MLA)
Saleh, Shokrya. Robust AIC with High Breakdown Scale Estimate. Journal of Applied Mathematics No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1039650
American Medical Association (AMA)
Saleh, Shokrya. Robust AIC with High Breakdown Scale Estimate. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1039650
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1039650