Robust AIC with High Breakdown Scale Estimate

Robust AIC with High Breakdown Scale Estimate

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...

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Main Author: Shokrya Saleh
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/286414
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spelling doaj-da163bf27def4cbea5dc7298a4bbc7ba2020-11-25T00:52:18ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/286414286414Robust AIC with High Breakdown Scale EstimateShokrya Saleh0Institute of Mathematical Sciences, University of Malaya, 50603 Kuala Lumpur, MalaysiaAkaike 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.http://dx.doi.org/10.1155/2014/286414
collection DOAJ
language English
format Article
sources DOAJ
author Shokrya Saleh
spellingShingle Shokrya Saleh
Robust AIC with High Breakdown Scale Estimate
Journal of Applied Mathematics
author_facet Shokrya Saleh
author_sort Shokrya Saleh
title Robust AIC with High Breakdown Scale Estimate
title_short Robust AIC with High Breakdown Scale Estimate
title_full Robust AIC with High Breakdown Scale Estimate
title_fullStr Robust AIC with High Breakdown Scale Estimate
title_full_unstemmed Robust AIC with High Breakdown Scale Estimate
title_sort robust aic with high breakdown scale estimate
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2014-01-01
description 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.
url http://dx.doi.org/10.1155/2014/286414
work_keys_str_mv AT shokryasaleh robustaicwithhighbreakdownscaleestimate
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