Defense Date


Document Type


Degree Name

Doctor of Philosophy


Systems Modeling and Analysis

First Advisor

QiQi Lu

Second Advisor

D'Arcy P. Mays

Third Advisor

Yanjun Qian

Fourth Advisor

Qin Wang


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.


© Mo Li

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Date of Submission


Available for download on Tuesday, October 13, 2026