Bayesian estimation in scale mixture of skew-normal linear mixed models using Hamiltonian Monte Carlo
64th ISI World Statistics Congress - Ottawa, Canada
Format: CPS Abstract
Keywords: bayesian_model, clustered-data, skewed
Session: CPS 56 - Statistical estimation VI
Tuesday 18 July 4 p.m. - 5:25 p.m. (Canada/Eastern)
In clinical trials, studies often present longitudinal or clustered data. These studies are commonly analyzed using linear mixed models, and for mathematical convenience, it is usually assumed that both random effect and error term follow normal distributions. These restrictive assumptions, however, may result in a lack of robustness against departures from the normal distribution and invalid statistical inferences. An interesting extension to make these models more flexible by accounting for skewness and heavy tails is considering the scale mixture of skew-normal class of distributions. Nevertheless, a practical problem may arise when modeling distributions derived from the skew-normal: the possibility that the maximum likelihood estimate of the parameter which regulates skewness diverges. In this work, this anomaly is illustrated, and an alternative Bayesian estimation via Hamiltonian Monte Carlo is proposed.