{"id":{"repo_id":"duquesne","oai_identifier":"oai:dsc.duq.edu:etd-1690"},"canonical_url":"https://search.dev.ndltd.org/etd/duquesne/oai:dsc.duq.edu:etd-1690","repository":{"repo_id":"duquesne","name":"Duquesne","base_url":"https://dsc.duq.edu/do/oai/"},"display":{"title":"Exploring autism prediction through logistic regression analysis with corrections for rare events data","abstract":"The study of rare events data in which observations of non-event outcomes far outnumber event outcomes makes inference under these circumstances quite difficult. Ideally, for a binary dependent variable, one would like sample data to contain enough observations from both outcome categories. With rare events data, however, this is usually impossible and/or costly to achieve with random sampling. This exploratory research aims to find a set of potential predictors that could be used to quantify a person's risk for developing autism spectrum disorder. A more efficient data collection strategy will be employed that allows for a smaller sample size of more meaningful data. Then, a statistical correction to the standard logistic regression model will be applied to yield adjusted predictions that take into account the prevalence of autism cases both in the sample data and in the population of interest.","abstract_html":"The study of rare events data in which observations of non-event outcomes far outnumber event outcomes makes inference under these circumstances quite difficult. Ideally, for a binary dependent variable, one would like sample data to contain enough observations from both outcome categories. With rare events data, however, this is usually impossible and/or costly to achieve with random sampling. This exploratory research aims to find a set of potential predictors that could be used to quantify a person's risk for developing autism spectrum disorder. A more efficient data collection strategy will be employed that allows for a smaller sample size of more meaningful data. Then, a statistical correction to the standard logistic regression model will be applied to yield adjusted predictions that take into account the prevalence of autism cases both in the sample data and in the population of interest.","abstract_has_math":false,"creators":["Hunter, Jennifer"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Computational Mathematics","degree_department":null,"school":null,"contributors":["John Kern","Frank D'Amico","James Schreiber"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-01T08:00:00Z","date_published":"2015-01-01T08:00:00Z","updated_at":"2026-07-24T02:09:46Z","subjects":["Pure sciences","Psychology","Autism","Logistic regression","Rare events"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dsc.duq.edu/etd/674","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John Kern","Frank D'Amico","James Schreiber"]},{"key":"dc:creator","label":"Author","values":["Hunter, Jennifer"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-08-03T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Immediate Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Pure sciences","Psychology","Autism","Logistic regression","Rare events"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dsc.duq.edu/etd/674"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The study of rare events data in which observations of non-event outcomes far outnumber event outcomes makes inference under these circumstances quite difficult. Ideally, for a binary dependent variable, one would like sample data to contain enough observations from both outcome categories. With rare events data, however, this is usually impossible and/or costly to achieve with random sampling. This exploratory research aims to find a set of potential predictors that could be used to quantify a person's risk for developing autism spectrum disorder. A more efficient data collection strategy will be employed that allows for a smaller sample size of more meaningful data. Then, a statistical correction to the standard logistic regression model will be applied to yield adjusted predictions that take into account the prevalence of autism cases both in the sample data and in the population of interest."]},{"key":"dc:title","label":"Title","values":["Exploring autism prediction through logistic regression analysis with corrections for rare events data"]}]}],"canonical_facts":{"dc:contributor":["John Kern","Frank D'Amico","James Schreiber"],"dc:creator":["Hunter, Jennifer"],"dc:date.available":["2018-08-03T07:00:00Z"],"dc:description.abstract":["The study of rare events data in which observations of non-event outcomes far outnumber event outcomes makes inference under these circumstances quite difficult. Ideally, for a binary dependent variable, one would like sample data to contain enough observations from both outcome categories. With rare events data, however, this is usually impossible and/or costly to achieve with random sampling. This exploratory research aims to find a set of potential predictors that could be used to quantify a person's risk for developing autism spectrum disorder. A more efficient data collection strategy will be employed that allows for a smaller sample size of more meaningful data. Then, a statistical correction to the standard logistic regression model will be applied to yield adjusted predictions that take into account the prevalence of autism cases both in the sample data and in the population of interest."],"dc:identifier":["https://dsc.duq.edu/etd/674"],"dc:language":["English"],"dc:subject":["Pure sciences","Psychology","Autism","Logistic regression","Rare events"],"dc:title":["Exploring autism prediction through logistic regression analysis with corrections for rare events data"],"thesis:degree_discipline":["Computational Mathematics"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:09:46Z"}