Dr. Ian Laga (Dept. of Mathematical Sciences, MSU) 

03/02/23  3:10pm

Abstract: 

In this work, we propose a subsampling procedure to decrease the computation time of zero-inflated models. Zero-inflated models are increasing in popularity and are vital to a variety of applications and disciplines. Performing variable selection, estimating parameters, and diagnosing model fit for zero-inflated models is often prohibitively slow, especially for large data sets and Bayesian models. We show that we can consistently estimate the intercept and slope parameters of both the zero and conditional models. Performance is evaluated using a spatial presence-only data set related to the number of female sex workers in four countries in sub-Saharan Africa.