# 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.