Doctoral Dissertations

Yi Zhang

Keywords and Phrases

Conditional Heteroskedasticity; Convolutional Neural Networks; Discrete Time Series; Markov Chain; Seasonality; Sleep Models

Abstract

“Due to fast developments of advanced sensors, count data sets have become ubiquitous in many fields. Modeling and forecasting such time series have generated great interest. Modeling can shed light on the behavior of the count series and to see how they are related to other factors such as the environmental conditions under which the data are generated. In this research, three approaches to modeling such count data are proposed.

First, a periodic autoregressive conditional Poisson (PACP) model is proposed as a natural generalization of the autoregressive conditional Poisson (ACP) model. By allowing for cyclical variations in the parameters of the model, it provides a way to explain the periodicity inherent in many count data series. For example, in epidemiology the prevalence of a disease may depend on the season. The autoregressive conditional Poisson hidden Markov model (ACP-HMM) is developed to deal with count data time series whose mean, conditional on the past, is a function of previous observations, with this relationship possibly determined by an unobserved process that switches its state or regime as time progresses. This model, in a sense, is the combination of the discrete version of the autoregressive conditional heteroscedastic (ARCH) formulation and the Poisson hidden Markov model. Both the above models address the frequently present serial correlation and the clustering of high or low counts observed in time series of count data, while at the same time allowing the underlying data generating mechanism to change cyclically or according to a hidden Markov process. Applications to empirical data sets show that these models provide a better fit than the standard ACP models. In addition to the above models, a modification of a zero-inflated Poisson model is used to analyze activity counts of the fruit fly. The model captures the dynamic structure of activity patterns and the fly's propensity to sleep. The obtained results when fed to a convolutional neural network provides the possibility of building a predictive model to identify fruit flies with short and long lifespans”--Abstract, page iv.

Advisor(s)

Samaranayake, V. A.

Committee Member(s)

Olbricht, Gayla R.
Paige, Robert L.
Gelles, Gregory M.
Wen, Xuerong Meggie
Thimgan, Matthew S.

Department(s)

Mathematics and Statistics

Degree Name

Ph. D. in Mathematics

Comments

Doctor of Philosophy in Mathematics with Statistics Emphasis

This research was solely supported by the National Institute of General Medical Sciences of the National Institute of Health under award number R15GM117507.

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2021

Journal article titles appearing in thesis/dissertation

  • Modeling Time Series of Count Data using a Periodic Autoregressive Conditional Poisson Model
  • Autoregressive Conditional Heteroskedastic Hidden Markov Model
  • Predicting Lifespan of Drosophila Melanogaster: A Novel Application of Convolutional Neural Networks and Zero- Inflated Autoregressive Conditional Poisson Model

Pagination

xii, 102 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2021 Yi Zhang, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11931

Electronic OCLC #

1286687010

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