{"id":{"repo_id":"cagliari","oai_identifier":"oai:iris.unica.it:11584/266587"},"canonical_url":"https://search.dev.ndltd.org/etd/cagliari/oai:iris.unica.it:11584/266587","repository":{"repo_id":"cagliari","name":"Università di Cagliari","base_url":"https://iris.unica.it/oai/request"},"display":{"title":"Parametric modeling of dependence of bivariate quantile regression residuals' signs","abstract":"In this thesis, we propose a non-parametric method to study the dependence of the quantiles of a multivariate response conditional on a set of covariates. We define a statistic that measures the conditional probability of concordance of the signs of the residuals of the conditional quantiles of each univariate response. The probability of concordance is bounded from below by the value of largest possible negative dependence and from above by that of largest possible positive dependence. The value corresponding to the case of independence is contained in the interior of that interval. We recommend two distinct regression methods to model the conditional probability of concordance. The first is a logistic regression with a logit link modified. The second one is a nonlinear regression method, where the outcome is modeled as a polynomial function of the linear predictor. Both are conceived to constrain the predicted probabilities to lie within the feasible range. The estimated probabilities can be tested against the values of largest possible dependence and independence. The method permits to capture important aspects of the dependence of multivariate responses and assess possible effects of covariates on such dependence. We use data on pulmonary disfunctions to illustrate the potential of the proposed method. We suggest also graphical tools for a correct interpretation of results.","abstract_html":"In this thesis, we propose a non-parametric method to study the dependence of the quantiles of a multivariate response conditional on a set of covariates. We define a statistic that measures the conditional probability of concordance of the signs of the residuals of the conditional quantiles of each univariate response. The probability of concordance is bounded from below by the value of largest possible negative dependence and from above by that of largest possible positive dependence. The value corresponding to the case of independence is contained in the interior of that interval. We recommend two distinct regression methods to model the conditional probability of concordance. The first is a logistic regression with a logit link modified. The second one is a nonlinear regression method, where the outcome is modeled as a polynomial function of the linear predictor. Both are conceived to constrain the predicted probabilities to lie within the feasible range. The estimated probabilities can be tested against the values of largest possible dependence and independence. The method permits to capture important aspects of the dependence of multivariate responses and assess possible effects of covariates on such dependence. We use data on pulmonary disfunctions to illustrate the potential of the proposed method. We suggest also graphical tools for a correct interpretation of results.","abstract_has_math":false,"creators":["COLUMBU, SILVIA"],"institution":"Università degli Studi di Cagliari","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-04-16","date_published":"2015-04-16","updated_at":"2026-07-24T01:30:07Z","subjects":["bivariate","bivariato","dependence structure","dipendenza del segno dei residui","quantic regression","regressione quantica","residual signs dependence","struttura di dipendenza","Settore SECS-S/01 - Statistica"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess","license:Non specificato"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11584/266587","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["COLUMBU, SILVIA"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-04-16"]},{"key":"dc:publisher","label":"Institution","values":["Università degli Studi di Cagliari"]},{"key":"dc:relation","label":"Dc Relation","values":["numberofpages:136"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["bivariate","bivariato","dependence structure","dipendenza del segno dei residui","quantic regression","regressione quantica","residual signs dependence","struttura di dipendenza","Settore SECS-S/01 - Statistica"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:Non specificato"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/11584/266587"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis, we propose a non-parametric method to study the dependence of the quantiles of a multivariate response conditional on a set of covariates. We define a statistic that measures the conditional probability of concordance of the signs of the residuals of the conditional quantiles of each univariate response. The probability of concordance is bounded from below by the value of largest possible negative dependence and from above by that of largest possible positive dependence. The value corresponding to the case of independence is contained in the interior of that interval. We recommend two distinct regression methods to model the conditional probability of concordance. The first is a logistic regression with a logit link modified. The second one is a nonlinear regression method, where the outcome is modeled as a polynomial function of the linear predictor. Both are conceived to constrain the predicted probabilities to lie within the feasible range. The estimated probabilities can be tested against the values of largest possible dependence and independence. The method permits to capture important aspects of the dependence of multivariate responses and assess possible effects of covariates on such dependence. We use data on pulmonary disfunctions to illustrate the potential of the proposed method. We suggest also graphical tools for a correct interpretation of results."]},{"key":"dc:title","label":"Title","values":["Parametric modeling of dependence of bivariate quantile regression residuals' signs"]}]}],"canonical_facts":{"dc:creator":["COLUMBU, SILVIA"],"dc:date":["2015-04-16"],"dc:description":["In this thesis, we propose a non-parametric method to study the dependence of the quantiles of a multivariate response conditional on a set of covariates. We define a statistic that measures the conditional probability of concordance of the signs of the residuals of the conditional quantiles of each univariate response. The probability of concordance is bounded from below by the value of largest possible negative dependence and from above by that of largest possible positive dependence. The value corresponding to the case of independence is contained in the interior of that interval. We recommend two distinct regression methods to model the conditional probability of concordance. The first is a logistic regression with a logit link modified. The second one is a nonlinear regression method, where the outcome is modeled as a polynomial function of the linear predictor. Both are conceived to constrain the predicted probabilities to lie within the feasible range. The estimated probabilities can be tested against the values of largest possible dependence and independence. The method permits to capture important aspects of the dependence of multivariate responses and assess possible effects of covariates on such dependence. We use data on pulmonary disfunctions to illustrate the potential of the proposed method. We suggest also graphical tools for a correct interpretation of results."],"dc:identifier":["http://hdl.handle.net/11584/266587"],"dc:language":["eng"],"dc:publisher":["Università degli Studi di Cagliari"],"dc:relation":["numberofpages:136"],"dc:rights":["info:eu-repo/semantics/openAccess","license:Non specificato"],"dc:subject":["bivariate","bivariato","dependence structure","dipendenza del segno dei residui","quantic regression","regressione quantica","residual signs dependence","struttura di dipendenza","Settore SECS-S/01 - Statistica"],"dc:title":["Parametric modeling of dependence of bivariate quantile regression residuals' signs"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-24T01:30:07Z"}