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2014 Robust AIC with High Breakdown Scale Estimate
Shokrya Saleh
J. Appl. Math. 2014: 1-7 (2014). DOI: 10.1155/2014/286414

Abstract

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.

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Shokrya Saleh. "Robust AIC with High Breakdown Scale Estimate." J. Appl. Math. 2014 1 - 7, 2014. https://doi.org/10.1155/2014/286414

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131487
MathSciNet: MR3261249
Digital Object Identifier: 10.1155/2014/286414

Rights: Copyright © 2014 Hindawi

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