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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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/286414 |
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| _version_ | 1849472880444702720 |
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| author | Shokrya Saleh |
| author_facet | Shokrya Saleh |
| author_sort | Shokrya Saleh |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-da163bf27def4cbea5dc7298a4bbc7ba |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-da163bf27def4cbea5dc7298a4bbc7ba2025-08-20T03:24:22ZengWileyJournal 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 |
| spellingShingle | Shokrya Saleh Robust AIC with High Breakdown Scale Estimate Journal of Applied Mathematics |
| title | 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_short | Robust AIC with High Breakdown Scale Estimate |
| title_sort | robust aic with high breakdown scale estimate |
| url | http://dx.doi.org/10.1155/2014/286414 |
| work_keys_str_mv | AT shokryasaleh robustaicwithhighbreakdownscaleestimate |