MS thesis defense (statistics)
Event Type:
Other
Speaker:
Danielle K Duran
Event Date:
Monday, May 1, 2017 -
3:30pm to 4:30pm
Location:
SMLC 352
Audience:
General PublicFaculty/StaffStudentsAlumni/Friends
Sponsor/s:
Stat group
Event Description:
Title: Comparison of two methods in estimating standard error of simulated moments estimators for generalized linear mixed models
Abstract: We consider standard error of the method of simulated moment (MSM)
estimator for generalized linear mixed models (GLMM). Parametric
bootstrap (PB) has been used to estimate the covariance matrix, in
which we use the estimates to generate the simulated moments. To
avoid the bias introduced by estimating the parameters and to deal
with the correlated observations, \citeA{lu:2012} proposed a
multi-stage block nonparametric bootstrap to estimate the standard
errors. In this research, we compare PB and nonparametric bootstrap
methods (NPB) in estimating the standard errors of MSM estimators
for GLMM. Simulation results show that when the group size is large,
NPB and PB perform similarly; when group size is medium, NPB
performs better than PB in estimating the mean. A data application
is considered to illustrate the methods discussed in this paper,
using productivity of plantation roses. The data application finds
that, the person caring for the roses is associated with the
productivity of those beds. Furthermore, we did an initial study in
applying random forests to predict the productivity of the rose
beds.