Bayesian inference in joint modelling of location and scale parameters of the \(t\) distribution for longitudinal data

DOI10.1016/j.jspi.2010.11.001zbMath1204.62040OpenAlexW2069638309MaRDI QIDQ622452

Wan-Lun Wang, Tsung I. Lin

Publication date: 31 January 2011

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2010.11.001

zbMATH Keywords

outliersmaximum likelihood estimationdata augmentationCholesky decompositionpredictive distributiondeviance information criterion

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items

A skew-normal mixture of joint location, scale and skewness models ⋮ Robust modeling of multivariate longitudinal data using modified Cholesky and hypersphere decompositions ⋮ Variable Selection in Heteroscedastic Regression Models Under General Skew-t Distributional Models Using Information Complexity ⋮ Semiparametric Bayesian inference for mean-covariance regression models ⋮ A semiparametric Bayesian approach to joint mean and variance models ⋮ Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions ⋮ Bayesian inference for joint location and scale nonlinear models with skew-normal errors ⋮ Covariance estimation: the GLM and regularization perspectives ⋮ Bayesian analysis of joint mean and covariance models for longitudinal data ⋮ A robust joint modeling approach for longitudinal data with informative dropouts ⋮ Joint modeling of location and scale parameters of the skew-normal distribution

Uses Software

• lme4

Cites Work

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