{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/10978"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/10978","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"On testing for a difference in two high-dimensional mean vectors.","abstract":"A common problem in multivariate statistical analysis involves testing for differences in the mean vectors from two populations with equal covariance matrices.This problem is considered well-posed when the sum of the two sample sizes is greater than the data dimension and, therefore, the traditional Hotelling's T2 test can be applied. In cases where the data dimension exceeds the sample-sizes sum minus two, the pooled sample covariance matrix is singular and, thus, nontraditional tests must be formulated. Using Monte Carlo simulations, we first contrast the powers of five hypothesis tests for two high-dimensional means that have been proposed in the statistical literature. We then examine the efficacy of linear dimension reduction derived from the singular value decomposition of the total data matrix and explore its effect on the powers of five tests when the tests are conducted with the dimension-reduced data. We then propose a new test for the difference in two high-dimensional mean vectors that combines aspects of the random subspaces and cluster subspaces tests to improve test power.","abstract_html":"A common problem in multivariate statistical analysis involves testing for differences in the mean vectors from two populations with equal covariance matrices.This problem is considered well-posed when the sum of the two sample sizes is greater than the data dimension and, therefore, the traditional Hotelling's T2 test can be applied. In cases where the data dimension exceeds the sample-sizes sum minus two, the pooled sample covariance matrix is singular and, thus, nontraditional tests must be formulated. Using Monte Carlo simulations, we first contrast the powers of five hypothesis tests for two high-dimensional means that have been proposed in the statistical literature. We then examine the efficacy of linear dimension reduction derived from the singular value decomposition of the total data matrix and explore its effect on the powers of five tests when the tests are conducted with the dimension-reduced data. We then propose a new test for the difference in two high-dimensional mean vectors that combines aspects of the random subspaces and cluster subspaces tests to improve test power.","abstract_has_math":false,"creators":["Worley, Whitney V., 1990-"],"institution":"Baylor University.","degree_name":"Ph.D.","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Young, Dean M."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-12","date_published":"2019-12","updated_at":"2026-07-24T01:08:13Z","subjects":["Multivariate statistics.","High dimensional mean vectors."],"languages":["en"],"rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2104/10978","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Young, Dean M."]},{"key":"dc:creator","label":"Author","values":["Worley, Whitney V., 1990-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-09-04T18:16:19Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-09-04T18:16:19Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Baylor University."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Multivariate statistics.","High dimensional mean vectors."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2104/10978"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A common problem in multivariate statistical analysis involves testing for differences in the mean vectors from two populations with equal covariance matrices.This problem is considered well-posed when the sum of the two sample sizes is greater than the data dimension and, therefore, the traditional Hotelling's T2 test can be applied. In cases where the data dimension exceeds the sample-sizes sum minus two, the pooled sample covariance matrix is singular and, thus, nontraditional tests must be formulated. Using Monte Carlo simulations, we first contrast the powers of five hypothesis tests for two high-dimensional means that have been proposed in the statistical literature. We then examine the efficacy of linear dimension reduction derived from the singular value decomposition of the total data matrix and explore its effect on the powers of five tests when the tests are conducted with the dimension-reduced data. We then propose a new test for the difference in two high-dimensional mean vectors that combines aspects of the random subspaces and cluster subspaces tests to improve test power."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["On testing for a difference in two high-dimensional mean vectors."]}]}],"canonical_facts":{"dc:contributor.advisor":["Young, Dean M."],"dc:creator":["Worley, Whitney V., 1990-"],"dc:date.accessioned":["2020-09-04T18:16:19Z"],"dc:date.available":["2020-09-04T18:16:19Z"],"dc:date.issued":["2019-12"],"dc:description.abstract":["A common problem in multivariate statistical analysis involves testing for differences in the mean vectors from two populations with equal covariance matrices.This problem is considered well-posed when the sum of the two sample sizes is greater than the data dimension and, therefore, the traditional Hotelling's T2 test can be applied. In cases where the data dimension exceeds the sample-sizes sum minus two, the pooled sample covariance matrix is singular and, thus, nontraditional tests must be formulated. Using Monte Carlo simulations, we first contrast the powers of five hypothesis tests for two high-dimensional means that have been proposed in the statistical literature. We then examine the efficacy of linear dimension reduction derived from the singular value decomposition of the total data matrix and explore its effect on the powers of five tests when the tests are conducted with the dimension-reduced data. We then propose a new test for the difference in two high-dimensional mean vectors that combines aspects of the random subspaces and cluster subspaces tests to improve test power."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2104/10978"],"dc:language.iso":["en"],"dc:rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"dc:subject":["Multivariate statistics.","High dimensional mean vectors."],"dc:title":["On testing for a difference in two high-dimensional mean vectors."],"dc:type":["Thesis"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["Baylor University."]},"updated_at":"2026-07-24T01:08:13Z"}