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Equivalence testing for mean vectors of multivariate normal populations
Clarkson, Elizabeth P.
Clarkson, Elizabeth P.
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Authors
Clarkson, Elizabeth P.
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Advisors
Hu, Xiaomi
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Issue Date
2010-05
Type
Dissertation
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Electronic dissertations
Electronic dissertations
Electronic dissertations
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Abstract
This dissertation examines the problem of comparing samples of multivariate normal data
from two populations and concluding whether the populations are equivalent; equivalence is
defined as the distance between the mean vectors of the two samples being less than a given
value.
Test statistics are developed for each of two cases using the ratio of the maximized
likelihood functions. Case 1 assumes both populations have a common known covariance
matrix. Case 2 assumes both populations have a common covariance matrix, but this covariance
matrix is a known matrix multiplied by an unknown scalar value. The power function and bias
of each of the test statistics is evaluated. Tables of critical values are provided.
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Description
Thesis (Ph.D.)--Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics and Statistics
Publisher
Wichita State University
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Copyright Elizabeth Clarkson, 2010. All rights reserved