Parametric Bootstrapping Predictive Estimator for Logistic Regression
Kunio Takezawa, タケザワ, クニオ, 竹澤, 邦夫
Journal of Advances in Mathematics and Computer Science · pp. 1–15 · Published 10 Jun 2019
10.9734/jamcs/2019/v32i530154Abstract
This paper proposes a method for constructing a predictive estimator for logistic regression. We make a provisional assumption that the predictive estimator is given by multiplying the maximum likelihood estimators by constants, which are estimated using a parametric bootstrap method. The relative merits of the maximum likelihood estimator and the predictive estimator produced by this method are determined by cross-validation. The results show that the predictive estimators derived by this method lead to a smaller deviance than that obtained by the maximum likelihood estimator in many instances.
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