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
Bayesian Inference for Lp-quantile regression models / Bernardi, M.; Bignozzi, V.; Petrella, Lea. - STAMPA. - (2016), pp. 1-6. (Intervento presentato al convegno 48th scientific meeting of the Italian Statistical Society -SIS: Innovazione & Società, Metodi Statistici per la valutazione tenutosi a Università degli Studi di Salerno - Campus universitario di Fisciano).
Bayesian Inference for Lp-quantile regression models
PETRELLA, Lea
2016
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| File | Dimensione | Formato | |
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