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 Keywords
- 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 Abstract Implementation of the Clean Water Act requires agencies to monitor aquatic sites on aregular 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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