"Bayesian Variable Selection For Semiparametric Logistic Regression" by Zainab Sami and Taha Alshaybawee

Article Title

Bayesian Variable Selection For Semiparametric Logistic Regression

Authors

Zainab Sami, Al-Qadisiyah University College of Administration and EconomicsFollow
Taha Alshaybawee, Al-Qadisiyah University College of Administration and Economics

Document Type

Article

Abstract

Lasso variable selection is an attractive approach to improve the prediction accuracy. Bayesian lasso approach is suggested to estimate and select the important variables for single index logistic regression model. Laplace distribution is set as prior to the coefficients vector and prior to the unknown link function (Gaussian process). A hierarchical Bayesian lasso semiparametric logistic regression model is constructed and MCMC algorithm is adopted for posterior inference. To evaluate the performance of the proposed method BSLLR is through comparing it to three existing methods BLR, BPR and BBQR. Simulation examples and numerical data are to be considered. The results indicate that the proposed method get the smallest bias, SD, MSE and MAE in simulation and real data. The proposed method BSLLR performs better than other methods.

Keywords

Logistic regression, Bayesian inference, Single index model, lasso, MCMC algorithm

Recommended Citation

Sami, Zainab and Alshaybawee, Taha (2021) "Bayesian Variable Selection For Semiparametric Logistic Regression," Al-Qadisiyah Journal of Pure Science: Vol. 26 : No. 5 , Article 5.
Available at: https://doi.org/10.29350/qjps.2021.26.5.1460