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Type of Document Dissertation Author Tang, Anqi Author's Email Address atang@stat.fsu.edu URN etd-03312011-233815 Title A Class of Mixed-Distribution Models with Applications in Financial Data Analysis Degree Doctor of Philosophy Department Statistics, Department of Advisory Committee
Advisor Name Title Xufeng Niu Committee Chair Fred Huffer Committee Member Wei Wu Committee Member Yingmei Cheng University Representative Keywords
- MCEM Algorithm
- Mixed-Distribution Models
- CEO Compensation
Date of Defense 2011-03-16 Availability unrestricted Abstract Statisticians often encounter data in the form of a combination of discrete and continuousoutcomes. A special case is zero-in
ated longitudinal data where the response variable has a
large portion of zeros. These data exhibit correlation because observations are obtained on
the same subjects over time. In this dissertation, we propose a two-part mixed distribution
model to model zero-in
ated longitudinal data. The rst part of the model is a logistic
regression model that models the probability of nonzero response; the other part is a linear
model that models the mean response given that the outcomes are not zeros. Random e ects
with AR(1) covariance structure are introduced into both parts of the model to allow serial
correlation and subject speci c e ect.
Estimating the two-part model is challenging because of high dimensional integration necessary
to obtain the maximum likelihood estimates. We propose a Monte Carlo EM algorithm
for estimating the maximum likelihood estimates of parameters. Through simulation study,
we demonstrate the good performance of the MCEM method in parameter and standard
error estimation.
To illustrate, we apply the two-part model with correlated random e ects and the model
with autoregressive random e ects to executive compensation data to investigate potential
determinants of CEO stock option grants.
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