{"id":{"repo_id":"gsu","oai_identifier":"oai:digitalcommons.georgiasouthern.edu:etd-1664"},"canonical_url":"https://search.dev.ndltd.org/etd/gsu/oai:digitalcommons.georgiasouthern.edu:etd-1664","repository":{"repo_id":"gsu","name":"Georgia Southern University","base_url":"https://digitalcommons.georgiasouthern.edu/do/oai/"},"display":{"title":"A Nonparametric Method for Ascertaining Change Points in Regression Regimes","abstract":"Of interest is the specific model called the joinpoint two regime regression or broken line model composed of one regression line and a horizontal ray. This is a very restricted but highly useful subset of the well-researched change point problem. The usual approach to a more general model was first presented by Quandt (1958) who found the maximum likelihood estimates of the slope, intercept and joinpoint by assuming that the error terms are generated under the usual assumptions, that is, from a normal distribution with constant variance and are uncorrelated. We develop a method that does not rely on this assumption, demonstrate its use on an example of proximity indexes of whale cow and calf pairs, and compare the new method to the Quandt estimates in a simulation study showing this new method performs adequately.","abstract_html":"Of interest is the specific model called the joinpoint two regime regression or broken line model composed of one regression line and a horizontal ray. This is a very restricted but highly useful subset of the well-researched change point problem. The usual approach to a more general model was first presented by Quandt (1958) who found the maximum likelihood estimates of the slope, intercept and joinpoint by assuming that the error terms are generated under the usual assumptions, that is, from a normal distribution with constant variance and are uncorrelated. We develop a method that does not rely on this assumption, demonstrate its use on an example of proximity indexes of whale cow and calf pairs, and compare the new method to the Quandt estimates in a simulation study showing this new method performs adequately.","abstract_has_math":false,"creators":["Rogers, Alfreda N."],"institution":null,"degree_name":"Master of Science in Mathematics (M.S.)","degree_level":"Thesis (open access)","degree_discipline":"Department of Mathematical Sciences","degree_department":null,"school":null,"contributors":["Martha Abell","Greg Knofczynski"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-05-01T07:00:00Z","date_published":"2010-05-01T07:00:00Z","updated_at":"2026-07-24T02:27:19Z","subjects":["ETD","Maximum likelihood","Change point","Moment match"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/664","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Martha Abell","Greg Knofczynski"]},{"key":"dc:creator","label":"Author","values":["Rogers, Alfreda N."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2013-10-17T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Mathematical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (open access)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mathematics (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ETD","Maximum likelihood","Change point","Moment match"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/664"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Of interest is the specific model called the joinpoint two regime regression or broken line model composed of one regression line and a horizontal ray. This is a very restricted but highly useful subset of the well-researched change point problem. The usual approach to a more general model was first presented by Quandt (1958) who found the maximum likelihood estimates of the slope, intercept and joinpoint by assuming that the error terms are generated under the usual assumptions, that is, from a normal distribution with constant variance and are uncorrelated. We develop a method that does not rely on this assumption, demonstrate its use on an example of proximity indexes of whale cow and calf pairs, and compare the new method to the Quandt estimates in a simulation study showing this new method performs adequately."]},{"key":"dc:title","label":"Title","values":["A Nonparametric Method for Ascertaining Change Points in Regression Regimes"]}]}],"canonical_facts":{"dc:contributor":["Martha Abell","Greg Knofczynski"],"dc:creator":["Rogers, Alfreda N."],"dc:date.available":["2013-10-17T07:00:00Z"],"dc:description.abstract":["Of interest is the specific model called the joinpoint two regime regression or broken line model composed of one regression line and a horizontal ray. This is a very restricted but highly useful subset of the well-researched change point problem. The usual approach to a more general model was first presented by Quandt (1958) who found the maximum likelihood estimates of the slope, intercept and joinpoint by assuming that the error terms are generated under the usual assumptions, that is, from a normal distribution with constant variance and are uncorrelated. We develop a method that does not rely on this assumption, demonstrate its use on an example of proximity indexes of whale cow and calf pairs, and compare the new method to the Quandt estimates in a simulation study showing this new method performs adequately."],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/664"],"dc:subject":["ETD","Maximum likelihood","Change point","Moment match"],"dc:title":["A Nonparametric Method for Ascertaining Change Points in Regression Regimes"],"thesis:degree_discipline":["Department of Mathematical Sciences"],"thesis:degree_level":["Thesis (open access)"],"thesis:degree_name":["Master of Science in Mathematics (M.S.)"]},"updated_at":"2026-07-24T02:27:19Z"}