DOI

10.17077/etd.efwr-xy0r

Document Type

Dissertation

Date of Degree

Fall 2018

Access Restrictions

Access restricted until 01/31/2021

Degree Name

PhD (Doctor of Philosophy)

Degree In

Statistics

First Advisor

Cowles, Mary Kathryn

Second Advisor

Zimmerman, Dale L.

First Committee Member

Bognar, Matthew

Second Committee Member

Smith, Brian

Third Committee Member

Tierney, Luke

Abstract

Water quality and river/stream ecosystems are important for all living creatures. To protect human health, aquatic life and the surrounding ecosystem, a considerable amount of time and money has been spent on sampling and monitoring streams and rivers. Water quality monitoring and analysis can help researchers predict and learn from natural processes in the environment and determine human impacts on an ecosystem. Measurements such as temperature, pH, nitrogen concentration, algae and fish count collected along the network are all important factors in water quality analysis. The main purposes of the statistical analysis in this thesis are (1) to assess the relationship between the variable measured in the water (response variable) and other variables that describe either the locations on/along the stream network or certain characteristics at each location (explanatory variable), and (2) to assess the degree of similarity between the response variable values measured at different locations of the stream, i.e. spatial dependence structure. It is commonly accepted that measurements taken at two locations close to each other should have more similarity than locations far away. However, this is not always true for observations from stream networks. Observations from two sites that do not share water flow could be independent of each other even if they are very close in terms of stream distance, especially those observations taken on objects that move passively with the water flow. To model stream network data correctly, it is important to quantify the strength of association between observations from sites that do not share water.

Keywords

Bayesian, Intrinsic Conditional Autoregressive Model, Stream Network Model

Pages

x, 109 pages

Bibliography

Includes bibliographical references (pages 107-109).

Copyright

Copyright © 2018 Yingying Liu

Available for download on Sunday, January 31, 2021

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