Partially linear censored regression models using heavy-tailed distributions: a Bayesian approach

DOI10.1016/J.STAMET.2013.10.003zbMath1486.62100OpenAlexW2001779587MaRDI QIDQ1731194

Reinaldo B. Arellano-Valle, Guillermo P. Ferreira, Luis Mauricio Castro, Victor Hugo Lachos

Publication date: 13 March 2019

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2013.10.003

zbMATH Keywords

Bayesian modelingscale mixtures of normal distributionsnonlinear regression modelcensored regression models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Censored data models (62N01) Bayesian inference (62F15)

Related Items (6)

Unnamed Item ⋮ Unnamed Item ⋮ A censored time series analysis for responses on the unit interval: an application to acid rain modeling ⋮ A non-iterative Bayesian sampling algorithm for censored Student-tlinear regression models ⋮ Semiparametric inference for the scale-mixture of normal partial linear regression model with censored data ⋮ Modelling financial time series based on heavy-tailed market microstructure models with scale mixtures of normal distributions

Uses Software

• WinBUGS
• SemiPar
• BayesDA

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