Work

Bayesian Inference for Lp-quantile regression models

Year: 2016

Type: book-chapter

Abstract: Lp–quantiles generalise quantiles and expectiles to account for the whole distribution of the random variable of interest. In this paper, we introduce the Lp– quantile regression model, we propose a collapsed Gibbs–sampler algorithm to make Bayesian inference on the regression parameters. We also provide some theoretical results concerning the posterior distribution of the regression parameters

Source: BOA (University of Milano-Bicocca)

Authors: Bernardi, M, Bignozzi, V, Petrella, L

Institutions: University of Padua, Sapienza University of Rome

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2463621856

FWCI: 1.176

Cites:

Cited by: 1

Related to: 10