Parametric Bootstrapping Predictive Estimator for Logistic Regression

Main Article Content

Kunio Takezawa


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.

Log-likelihood, future data, predictive estimator, logistic regression, maximum likelihood estimator, parametric bootstrap method.

Article Details

How to Cite
Takezawa, K. (2019). Parametric Bootstrapping Predictive Estimator for Logistic Regression. Journal of Advances in Mathematics and Computer Science, 32(5), 1-15.
Original Research Article


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