The next event is the 2016 Annual Business Meeting, April 27, 2016 (Wed).
Speaker: Dr. Mingan Yang, Asst Prof, Div of Epi & Biostatistics, SDSU
Date: April 27, 2016 (Wednesday)
Time: 5:30 to 8:00PM
6:00-7:00pm: Dr. Yang’s presentation
7:30–8:00pm: Business Meeting
Location: Building CB2/1110, Pfizer La Jolla Campus,
10770 Science Center Drive, San Diego, CA 92121
The meeting, including dinner, are free to all San Diego Chapter members!
$0: SDASA members, $5.00 student/retiree non-members, $10 other non-members.
Signup and pay at https://www.123signup.com/register?id=prdgm
Included in this year’s Business Meeting:
- Chapter news updates
- Keynote presentation by Dr. Mingan Yang
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution, such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effect models, the random effects generally have a non-zero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and inter-laboratory rodent uterotrophic bioassay.