RT - Journal Article
T1 - Bayesian Analysis of Augmented Mixed Beta Models with Skew-Normal Random Effects
JF - srtc-jsri
YR - 2017
JO - srtc-jsri
VO - 14
IS - 1
UR - http://jsri.srtc.ac.ir/article-1-256-en.html
SP - 101
EP - 118
K1 - Augmented beta regression
K1 - beta distribution
K1 - mixed models
K1 - Bayesian approach
K1 - skew-normal distribution.
AB - Many studies in different areas include data in the form of rates or proportions that should be analyzed. The data may also accept values zero and one. Augmented beta regression models are an appropriate choice for continuous response variables in the closed unit interval [0,1]. The data in this model are based on a combination of three distributions, degenerate distribution at 0 and 1, and a beta density in (0,1). The random effects are usually added to the model for accommodating the data structures as well as correlation impacts. In most of these models, the random effects are generally assumed to be normally distributed, while this assumption is frequently violated in applied studies. In this paper, the augmented mixed beta regression model with skew-normal distributed random effects is presented. A Bayesian approach is adopted for parameter estimation using Markov Chain Monte Carlo method. The proposed model is applied to analyze a real data set from Labor Force Survey.
LA eng
UL http://jsri.srtc.ac.ir/article-1-256-en.html
M3 10.18869/acadpub.jsri.14.1.101
ER -