This thesis focuses on clustering fifteen Zernike coefficients using the method of clustering of linear regression models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters. The Bootstrap method is used to estimate the uncertainty on the number of clusters. These fifteen Zernike coefficients are clustered into four clusters with a 90% confidence interval of the number of clusters being (2, 5).
","abstract_html":"<p>This thesis focuses on clustering fifteen Zernike coefficients using the method of clustering of linear regression models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters. The Bootstrap method is used to estimate the uncertainty on the number of clusters. These fifteen Zernike coefficients are clustered into four clusters with a 90% confidence interval of the number of clusters being (2, 5).</p>","abstract_has_math":false,"creators":["Bao, Weichao"],"institution":null,"degree_name":"M.S.P.H.","degree_level":"Campus Access Thesis","degree_discipline":"Epidemiology and Biostatistics","degree_department":null,"school":null,"contributors":["Hongmei Zhang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01T08:00:00Z","date_published":"2010-01-01T08:00:00Z","updated_at":"2026-07-24T04:37:08Z","subjects":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"languages":[],"rights":["© 2010, Weichao Bao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/134","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hongmei Zhang"]},{"key":"dc:creator","label":"Author","values":["Bao, Weichao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Epidemiology and Biostatistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Campus Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S.P.H."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© 2010, Weichao Bao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/134"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis focuses on clustering fifteen Zernike coefficients using the method of clustering of linear regression models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters. The Bootstrap method is used to estimate the uncertainty on the number of clusters. These fifteen Zernike coefficients are clustered into four clusters with a 90% confidence interval of the number of clusters being (2, 5).
"]},{"key":"dc:title","label":"Title","values":["Clustering Analysis of Zernike Coefficients From High Order Aberration Patients"]}]}],"canonical_facts":{"dc:contributor":["Hongmei Zhang"],"dc:creator":["Bao, Weichao"],"dc:description.abstract":["This thesis focuses on clustering fifteen Zernike coefficients using the method of clustering of linear regression models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters. The Bootstrap method is used to estimate the uncertainty on the number of clusters. These fifteen Zernike coefficients are clustered into four clusters with a 90% confidence interval of the number of clusters being (2, 5).
"],"dc:identifier":["https://scholarcommons.sc.edu/etd/134"],"dc:rights":["© 2010, Weichao Bao"],"dc:subject":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"dc:title":["Clustering Analysis of Zernike Coefficients From High Order Aberration Patients"],"thesis:degree_discipline":["Epidemiology and Biostatistics"],"thesis:degree_level":["Campus Access Thesis"],"thesis:degree_name":["M.S.P.H."]},"updated_at":"2026-07-24T04:37:08Z"}