Discipline: Mathematics & Statistics
Subcategory: STEM Research
- North Carolina Central University
Co-Author(s): Paul J. Smith, University of Maryland, College Park, MD
The generalized linear mixed model (GLMM) extends classical regression analysis to non-normal, correlated response data. Because inference for GLMMs can be computationally difficult, simplifying distributional assumptions are often made. We focus on the robustness of estimators when a main component of the model, the random-effects distribution, is misspecified. Results for the maximum likelihood estimators (MLEs) of the Poisson-inverse Gaussian model are presented.
Funder Acknowledgement(s): This work was supported by National Science Foundation Grant #1700235. Additional support was provided by a faculty research grant from North Carolina Central University.
Faculty Advisor: None Listed,