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Corrected version of AIC for selecting multivariate normal linear regression models in a general nonnormal case


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  • Yanagihara, Hirokazu
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    This paper deals with the bias reduction of Akaike information criterion (AIC) for selecting variables in multivariate normal linear regression models when the true distribution of observation is an unknown nonnormal distribution. We propose a corrected version of AIC which is partially constructed by the jackknife method and is adjusted to the exact unbiased estimator of the risk when the candidate model includes the true model. It is pointed out that the influence of nonnormality in the bias of our criterion is smaller than the ones in AIC and TIC. We verify that our criterion is better than the AIC, TIC and EIC by conducting numerical experiments.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 97 (2006)
    Issue (Month): 5 (May)
    Pages: 1070-1089

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    Handle: RePEc:eee:jmvana:v:97:y:2006:i:5:p:1070-1089

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    Keywords: Bias reduction Influence of nonnormality Kullback-Leibler information Jackknife method Model misspecification Normal assumption Predicted residuals Selection of variables Robustness;


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    Cited by:
    1. Christopher Withers & Saralees Nadarajah, 2013. "Calibration with low bias," Statistical Papers, Springer, vol. 54(2), pages 371-379, May.
    2. Withers, Christopher S. & Nadarajah, Saralees, 2011. "Estimates of low bias for the multivariate normal," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1635-1647, November.


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