We describe and compare methods for constructing confidence intervals for quantile regression coefficients. We consider methods based on resampling, sparsity estimation, and test-inversion. In the latter group, along with the popular rank-score, we include methods based on linear and logistic regression that exploit the direct relationship between quantile function and probability functions. These might prove practical alternatives to other more popular approaches and can be applied to dependent data, as those that arise in longitudinal, cluster, spatial, and complex survey designs. Results of a simulation study seem to indicate that they may have correct coverage and similar or sometimes narrower confidence intervals than the other methods considered.
","abstract_html":"<p>We describe and compare methods for constructing confidence intervals for quantile regression coefficients. We consider methods based on resampling, sparsity estimation, and test-inversion. In the latter group, along with the popular rank-score, we include methods based on linear and logistic regression that exploit the direct relationship between quantile function and probability functions. These might prove practical alternatives to other more popular approaches and can be applied to dependent data, as those that arise in longitudinal, cluster, spatial, and complex survey designs. Results of a simulation study seem to indicate that they may have correct coverage and similar or sometimes narrower confidence intervals than the other methods considered.</p>","abstract_has_math":false,"creators":["Wu, Junlong"],"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":"2026-07-24T04:37:34Z","subjects":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability","bisection method","confidence interval","dependent data","logistic regression","quantile regression"],"languages":[],"rights":["© 2011, Junlong Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/560","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":["Wu, Junlong"]}]},{"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","bisection method","confidence interval","dependent data","logistic regression","quantile regression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© 2011, Junlong Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/560"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["We describe and compare methods for constructing confidence intervals for quantile regression coefficients. We consider methods based on resampling, sparsity estimation, and test-inversion. In the latter group, along with the popular rank-score, we include methods based on linear and logistic regression that exploit the direct relationship between quantile function and probability functions. These might prove practical alternatives to other more popular approaches and can be applied to dependent data, as those that arise in longitudinal, cluster, spatial, and complex survey designs. Results of a simulation study seem to indicate that they may have correct coverage and similar or sometimes narrower confidence intervals than the other methods considered.
"]},{"key":"dc:title","label":"Title","values":["Methods For Constructing Confidence Intervals For Quantile Regression Coefficients"]}]}],"canonical_facts":{"dc:contributor":["Matteo Bottai"],"dc:creator":["Wu, Junlong"],"dc:description.abstract":["We describe and compare methods for constructing confidence intervals for quantile regression coefficients. We consider methods based on resampling, sparsity estimation, and test-inversion. In the latter group, along with the popular rank-score, we include methods based on linear and logistic regression that exploit the direct relationship between quantile function and probability functions. These might prove practical alternatives to other more popular approaches and can be applied to dependent data, as those that arise in longitudinal, cluster, spatial, and complex survey designs. Results of a simulation study seem to indicate that they may have correct coverage and similar or sometimes narrower confidence intervals than the other methods considered.
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