DOI
https://doi.org/10.25772/A8TG-0Q18
Defense Date
2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Systems Modeling and Analysis
First Advisor
QiQi Lu
Second Advisor
D'Arcy P. Mays
Third Advisor
Yanjun Qian
Fourth Advisor
Qin Wang
Abstract
This dissertation develops two changepoint tests for serially correlated ordinal categorical time series.
A cumulative sum type test is devised to test for a single changepoint in a correlated categorical data sequence, which is constructed from a latent Gaussian process through clipping techniques. A sequential parameter estimation procedure is proposed to estimate the model parameters. The method is illustrated via simulations and applied to a real categorized rainfall time series from Albuquerque, New Mexico.
The second changepoint test is a likelihood ratio type test based on a marginalized transition model. This model permits likelihood inference and the serial dependence is modeled via a first-order Markov chain. An efficient algorithm is proposed to obtain the maximum likelihood estimates. The Lu and Wang (2012) changepoint detection method for annual frequencies of sky-cloudiness conditions at Fort St. John Airport in Canada is extended to hourly cloud cover data.
Rights
© Mo Li
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
10-14-2021
Included in
Applied Statistics Commons, Categorical Data Analysis Commons, Longitudinal Data Analysis and Time Series Commons, Statistical Methodology Commons