{"id":{"repo_id":"qucosa-diss","oai_identifier":"oai:qucosa:de:qucosa:72875"},"canonical_url":"https://search.dev.ndltd.org/etd/qucosa-diss/oai:qucosa:de:qucosa:72875","repository":{"repo_id":"qucosa-diss","name":"QUCOSA","base_url":"http://www.qucosa.de/oai/"},"display":{"title":"Logistic Regression for Prospectivity Modeling","abstract":"The thesis proposes a method for automated model selection using a logistic regression model in the context of prospectivity modeling, i.e. the exploration of minearlisations. This kind of data is characterized by a rare positive event and a large dataset. We adapted and combined the two statistical measures Wald statistic and Bayes' information criterion making it suitable for the processing of large data and a high number of variables that emerge in the nonlinear setting of logistic regression. The obtained models of our suggested method are parsimonious allowing for an interpretation and information gain. The advantages of our method are shown by comparing it to another model selection method and to arti cial neural networks on several datasets. Furthermore we introduced a possibility to induce spatial dependencies which are important in such geological settings.","abstract_html":"The thesis proposes a method for automated model selection using a logistic regression model in the context of prospectivity modeling, i.e. the exploration of minearlisations. This kind of data is characterized by a rare positive event and a large dataset. We adapted and combined the two statistical measures Wald statistic and Bayes' information criterion making it suitable for the processing of large data and a high number of variables that emerge in the nonlinear setting of logistic regression. The obtained models of our suggested method are parsimonious allowing for an interpretation and information gain. The advantages of our method are shown by comparing it to another model selection method and to arti cial neural networks on several datasets. Furthermore we introduced a possibility to induce spatial dependencies which are important in such geological settings.","abstract_has_math":false,"creators":["Kost, Samuel"],"institution":"TU Bergakademe Freiberg","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Rheinbach, Oliver","Schaeben, Helmut"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-11-13","date_published":"2020-11-13","updated_at":"2026-07-24T03:56:21Z","subjects":["Logistische Regression","Statistisches Lernen","Modellauswahl","Prospectivity Modeling","Logistic Regression","Statistical Learning","Model Selection"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rheinbach, Oliver","Schaeben, Helmut"]},{"key":"dc:creator","label":"Author","values":["Kost, Samuel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Technische Universität Bergakademie Freiberg"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["TU Bergakademe Freiberg"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Logistische Regression","Statistisches Lernen","Modellauswahl","Prospectivity Modeling","Logistic Regression","Statistical Learning","Model Selection"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The thesis proposes a method for automated model selection using a logistic regression model in the context of prospectivity modeling, i.e. the exploration of minearlisations. This kind of data is characterized by a rare positive event and a large dataset. We adapted and combined the two statistical measures Wald statistic and Bayes' information criterion making it suitable for the processing of large data and a high number of variables that emerge in the nonlinear setting of logistic regression. The obtained models of our suggested method are parsimonious allowing for an interpretation and information gain. The advantages of our method are shown by comparing it to another model selection method and to arti cial neural networks on several datasets. Furthermore we introduced a possibility to induce spatial dependencies which are important in such geological settings."]},{"key":"dc:title","label":"Title","values":["Logistic Regression for Prospectivity Modeling"]}]}],"canonical_facts":{"dc:contributor":["Rheinbach, Oliver","Schaeben, Helmut"],"dc:creator":["Kost, Samuel"],"dc:description.abstract":["The thesis proposes a method for automated model selection using a logistic regression model in the context of prospectivity modeling, i.e. the exploration of minearlisations. This kind of data is characterized by a rare positive event and a large dataset. We adapted and combined the two statistical measures Wald statistic and Bayes' information criterion making it suitable for the processing of large data and a high number of variables that emerge in the nonlinear setting of logistic regression. The obtained models of our suggested method are parsimonious allowing for an interpretation and information gain. The advantages of our method are shown by comparing it to another model selection method and to arti cial neural networks on several datasets. Furthermore we introduced a possibility to induce spatial dependencies which are important in such geological settings."],"dc:publisher":["Technische Universität Bergakademie Freiberg"],"dc:subject":["Logistische Regression","Statistisches Lernen","Modellauswahl","Prospectivity Modeling","Logistic Regression","Statistical Learning","Model Selection"],"dc:title":["Logistic Regression for Prospectivity Modeling"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["TU Bergakademe Freiberg"]},"updated_at":"2026-07-24T03:56:21Z"}