Bayesian bootstrap adaptive lasso estimators of regression models. Issue 8 (24th May 2021)

• Record Type: Journal Article
• Authors: Li, Bohan; Wu, Juan
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 8(2021)
• Page Start: 1651
• Page End: 1680
• Publication Date: 2021-05-24
• Subjects: Bayesian bootstrap -- adaptive lasso -- variable selection -- oracle properties; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1865959
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: This paper proposes a modified adaptive lasso method by the Bayesian bootstrap (BBAL) and approximates the posterior distributions of parameters for a linear and a logistic regression model, respectively. The BBAL estimators are proved to have asymptotic and Oracle properties and they are acquired by the coordinate descent algorithm which could get the solutions at the grid of values of the penalty parameter λ . Three numerical experiments are conducted to demonstrate the BBAL method. Test results show the consistency of the variable selection and result in more robust estimators. And we use the median coefficients of the BBAL estimators to do the prediction with a medical dataset.