Title page for ETD etd-09262007-153618

Type of Document Dissertation
Author Li, Zhengrong
URN etd-09262007-153618
Title Model-based Tests for Standards Evaluation and Biological Assessments
Degree PhD
Department Statistics
Advisory Committee
Advisor Name Title
Smith, Eric P. Committee Chair
Morgan, John P. Committee Member
Prins, Samantha C. Bates Committee Member
Yagow, Gene Committee Member
Ye, Keying Committee Member
  • model-based tests
  • water quality assessment
  • random effects models
  • regression-based test
  • redunancy analysis
  • reduced-rank analysis
  • fixed effects models
Date of Defense 2006-08-15
Availability unrestricted
Implementation of the Clean Water Act requires agencies to monitor aquatic sites on a

regular basis and evaluate the quality of these sites. Sites are evaluated individually even

though there may be numerous sites within a watershed. In some cases, sampling

frequency is inadequate and the evaluation of site quality may have low reliability. This

dissertation evaluates testing procedures for determination of site quality based on modelbased

procedures that allow for other sites to contribute information to the data from the

test site. Test procedures are described for situations that involve multiple measurements

from sites within a region and single measurements when stressor information is

available or when covariates are used to account for individual site differences.

Tests based on analysis of variance methods are described for fixed effects and

random effects models. The proposed model-based tests compare limits (tolerance limits

or prediction limits) for the data with the known standard. When the sample size for the

test site is small, using model-based tests improves the detection of impaired sites. The

effects of sample size, heterogeneity of variance, and similarity between sites are

discussed. Reference-based standards and corresponding evaluation of site quality are

also considered.

Regression-based tests provide methods for incorporating information from other

sites when there is information on stressors or covariates.

Extension of some of the methods to multivariate biological observations and

stressors is also discussed. Redundancy analysis is used as a graphical method for

describing the relationship between biological metrics and stressors. A clustering method

for finding stressor-response relationships is presented and illustrated using data from the

Mid-Atlantic Highlands. Multivariate elliptical and univariate regions for assessment of

site quality are discussed.

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