Date of Award
Summer 6-22-2011
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
First Advisor
Dr. Jun Han
Second Advisor
Dr. Yuanhui Xiao
Third Advisor
Dr. Yichuan Zhao
Abstract
This paper proposes a mixture of experts recurrent events model. This general model accommodates an unobservable frailty variable, intervention effect, influence of accumulating event occurrences, and covariate effects. A latent class variable is utilized to deal with a heterogeneous population and associated covariates. A homogeneous nonparametric baseline hazard and heterogeneous parametric covariate effects are assumed. Maximum likelihood principle is employed to obtain parameter estimates. Since the frailty variable and latent classes are unobserved, an estimation procedure is derived through the EM algorithm. A simulated data set is generated to illustrate the data structure of recurrent events for a heterogeneous population.
DOI
https://doi.org/10.57709/2245401
Recommended Citation
Brooks, Timesha U., "Estimation Algorithm for Mixture of Experts Recurrent Event Model." Thesis, Georgia State University, 2011.
doi: https://doi.org/10.57709/2245401