Regularized Bayesian quantile regression
| Autor: | Salaheddine El Adlouni, André St-Hilaire, Garba Salaou |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Elastic net regularization Statistics::Theory 010504 meteorology & atmospheric sciences Bayesian probability Markov chain Monte Carlo Bayesian inference 01 natural sciences Statistics::Computation Quantile regression 010104 statistics & probability symbols.namesake Lasso (statistics) Modeling and Simulation Statistics Econometrics symbols Statistics::Methodology 0101 mathematics Bayesian linear regression 0105 earth and related environmental sciences Mathematics Quantile |
| Zdroj: | Communications in Statistics - Simulation and Computation. 47:277-293 |
| ISSN: | 1532-4141 0361-0918 |
| DOI: | 10.1080/03610918.2017.1280830 |
| Popis: | A number of nonstationary models have been developed to estimate extreme events as function of covariates. A quantile regression (QR) model is a statistical approach intended to estimate and conduct inference about the conditional quantile functions. In this article, we focus on the simultaneous variable selection and parameter estimation through penalized quantile regression. We conducted a comparison of regularized Quantile Regression model with B-Splines in Bayesian framework. Regularization is based on penalty and aims to favor parsimonious model, especially in the case of large dimension space. The prior distributions related to the penalties are detailed. Five penalties (Lasso, Ridge, SCAD0, SCAD1 and SCAD2) are considered with their equivalent expressions in Bayesian framework. The regularized quantile estimates are then compared to the maximum likelihood estimates with respect to the sample size. A Markov Chain Monte Carlo (MCMC) algorithms are developed for each hierarchical model to simu... |
| Databáze: | OpenAIRE |
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