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Law of iterated logarithm and consistent model selection criterion in logistic regression

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  • Qian, Guoqi
  • Field, Chris

Abstract

In this paper we establish a law of iterated logarithm for the maximum likelihood estimator of the parameters in a logistic regression model with canonical link. This result establishes the strong consistency of some relevant model selection criteria. For a model selection criterion whose objective function consists of a minus log-likelihood like quantity and a penalty term, we have shown that it will select the simplest correct model almost surely if the penalty term is an increasing function of the model dimension and has an order higher than O(loglog n).

Suggested Citation

  • Qian, Guoqi & Field, Chris, 2002. "Law of iterated logarithm and consistent model selection criterion in logistic regression," Statistics & Probability Letters, Elsevier, vol. 56(1), pages 101-112, January.
    • Handle: RePEc:eee:stapro:v:56:y:2002:i:1:p:101-112
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    Citations

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    Cited by:

    1. Guoqi Qian & Yuehua Wu & Qing Shao, 2009. "A Procedure for Estimating the Number of Clusters in Logistic Regression Clustering," Journal of Classification, Springer;The Classification Society, vol. 26(2), pages 183-199, August.
    2. Jin, Man & Fang, Yixin & Zhao, Lincheng, 2005. "Variable selection in generalized linear models with canonical link functions," Statistics & Probability Letters, Elsevier, vol. 71(4), pages 371-382, March.
    3. Mukolo, Abraham & Cooil, Bruce & Victor, Bart, 2015. "The effects of utility evaluations, biomedical knowledge and modernization on intention to exclusively use biomedical health facilities among rural households in Mozambique," Social Science & Medicine, Elsevier, vol. 138(C), pages 225-233.

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