Bayesian estimation and variables selection for binary composite quantile regression
Abstract
In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite quantile regression model when the response variable is binary. For selecting variables in binary composite quantile regression lasso the adaptive lasso penalty is derived in a Bayesian framework. Simulation study and real data examples are used to examine the performance of the proposed methods compared to the other existing methods. We conclude that the proposed method is comparable.
Full Text:
PDFDOI: http://dx.doi.org/10.21533/pen.v8i2.1384
Refbacks
- There are currently no refbacks.
Copyright (c) 2020 Taha Alshaybawee, Fadil Hamid Hadi Alhusseini, Fadil Hamid Hadi Alhusseini, Ahmad Naeem Flaih, Ahmad Naeem Flaih
This work is licensed under a Creative Commons Attribution 4.0 International License.
ISSN: 2303-4521
Digital Object Identifier DOI: 10.21533/pen
This work is licensed under a Creative Commons Attribution 4.0 International License