AU - Fallah Mohsenkhani, Zohreh
AU - Mohammadzadeh, Mohsen
AU - Baghfalaki, Taban
TI - Bayesian Analysis of Augmented Mixed Beta Models with Skew-Normal Random Effects
PT - JOURNAL ARTICLE
TA - srtc-jsri
JN - srtc-jsri
VO - 14
VI - 1
IP - 1
4099 - http://jsri.srtc.ac.ir/article-1-256-en.html
4100 - http://jsri.srtc.ac.ir/article-1-256-en.pdf
SO - srtc-jsri 1
ABĀ - 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.
CP - IRAN
IN -
LG - eng
PB - srtc-jsri
PG - 101
PT - Research
YR - 2017