The underlying goal of the work involved in this dissertation is to increase the use of quantile regression in the health sciences. Quantile regression is a flexible analytic technique introduced by Koenker and Basset in 1978 that has traditionally been used in econometrics. Despite its many advantages, quantile regression has seen somewhat limited use in the health sciences. In order for quantile regression to be more widely used, the complementary tools available for use with linear regression also need to be available for use with quantile regression. The first two papers contained in this dissertation propose new methods for two of these complementary tools of interest to researchers, power/sample size calculation, and tests for lack of fit. As more studies in the health sciences use quantile regression the method will achieve greater exposure and researchers will become more comfortable with its use and more familiar with its advantages. To further increase the awareness of quantile regression in the health sciences, the final paper in this dissertation is a data analysis project examining the quantile effects of prenatal care utilization, maternal smoking, and hypertension on infant birth weight in South Carolina.
","abstract_html":"<p>The underlying goal of the work involved in this dissertation is to increase the use of quantile regression in the health sciences. Quantile regression is a flexible analytic technique introduced by Koenker and Basset in 1978 that has traditionally been used in econometrics. Despite its many advantages, quantile regression has seen somewhat limited use in the health sciences. In order for quantile regression to be more widely used, the complementary tools available for use with linear regression also need to be available for use with quantile regression. The first two papers contained in this dissertation propose new methods for two of these complementary tools of interest to researchers, power/sample size calculation, and tests for lack of fit. As more studies in the health sciences use quantile regression the method will achieve greater exposure and researchers will become more comfortable with its use and more familiar with its advantages. To further increase the awareness of quantile regression in the health sciences, the final paper in this dissertation is a data analysis project examining the quantile effects of prenatal care utilization, maternal smoking, and hypertension on infant birth weight in South Carolina.</p>","abstract_has_math":false,"creators":["Ortaglia, Andrew"],"institution":null,"degree_name":"Ph.D.","degree_level":"Campus Access Dissertation","degree_discipline":"Epidemiology and Biostatistics","degree_department":null,"school":null,"contributors":["Matteo Bottai"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-01-01T08:00:00Z","date_published":"2011-01-01T08:00:00Z","updated_at":"2026-07-24T04:37:49Z","subjects":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability","Quantile Regression"],"languages":[],"rights":["© 2011, Andrew Ortaglia"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/551","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Matteo Bottai"]},{"key":"dc:creator","label":"Author","values":["Ortaglia, Andrew"]}]},{"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 Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability","Quantile Regression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© 2011, Andrew Ortaglia"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/551"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The underlying goal of the work involved in this dissertation is to increase the use of quantile regression in the health sciences. Quantile regression is a flexible analytic technique introduced by Koenker and Basset in 1978 that has traditionally been used in econometrics. Despite its many advantages, quantile regression has seen somewhat limited use in the health sciences. In order for quantile regression to be more widely used, the complementary tools available for use with linear regression also need to be available for use with quantile regression. The first two papers contained in this dissertation propose new methods for two of these complementary tools of interest to researchers, power/sample size calculation, and tests for lack of fit. As more studies in the health sciences use quantile regression the method will achieve greater exposure and researchers will become more comfortable with its use and more familiar with its advantages. To further increase the awareness of quantile regression in the health sciences, the final paper in this dissertation is a data analysis project examining the quantile effects of prenatal care utilization, maternal smoking, and hypertension on infant birth weight in South Carolina.
"]},{"key":"dc:title","label":"Title","values":["Inferential Methods For Quantile Regression With Applications to Infant Birth Weight"]}]}],"canonical_facts":{"dc:contributor":["Matteo Bottai"],"dc:creator":["Ortaglia, Andrew"],"dc:description.abstract":["The underlying goal of the work involved in this dissertation is to increase the use of quantile regression in the health sciences. Quantile regression is a flexible analytic technique introduced by Koenker and Basset in 1978 that has traditionally been used in econometrics. Despite its many advantages, quantile regression has seen somewhat limited use in the health sciences. In order for quantile regression to be more widely used, the complementary tools available for use with linear regression also need to be available for use with quantile regression. The first two papers contained in this dissertation propose new methods for two of these complementary tools of interest to researchers, power/sample size calculation, and tests for lack of fit. As more studies in the health sciences use quantile regression the method will achieve greater exposure and researchers will become more comfortable with its use and more familiar with its advantages. To further increase the awareness of quantile regression in the health sciences, the final paper in this dissertation is a data analysis project examining the quantile effects of prenatal care utilization, maternal smoking, and hypertension on infant birth weight in South Carolina.
"],"dc:identifier":["https://scholarcommons.sc.edu/etd/551"],"dc:rights":["© 2011, Andrew Ortaglia"],"dc:subject":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability","Quantile Regression"],"dc:title":["Inferential Methods For Quantile Regression With Applications to Infant Birth Weight"],"thesis:degree_discipline":["Epidemiology and Biostatistics"],"thesis:degree_level":["Campus Access Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T04:37:49Z"}