{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/16166"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/16166","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Modified half logistic - exponentiated Kumaraswamy distribution and applications","abstract":"The statistical model of mixture distributions has become an important tool for analyzing complicated phenomena in the real world. This approach has a wide range of applications in the fields of biology, medicine, environment and engineering. Based on the characteristics of the Exponentiated Kumaraswamy model, researchers have carried out a series of explorations and studies. However, in order to provide a more appropriate model to support real-life applications and to make our data analysis more reliable, it is still meaningful to explore new models. Therefore, in this thesis, we propose a novel mixture model, which is a modified one based on the combination of the Type-II Half Logistic distribution and the Exponentiated Kumaraswamy distribution. We defined it as the Modified Half Logistic - Exponentiated Kumaraswamy (MHL-EK) distribution. Then, we will study its statistical properties, such as quantile function, moments, incomplete moments, etc. Next, maximum likelihood estimation will be used for the parameter estimation of the MHL-EK model. We will also present a related simulation study. Finally, we apply this model to three real data sets and give our analysis for them. The results show that the MHL-EK distribution is a relatively suitable model among the candidate models.","abstract_html":"The statistical model of mixture distributions has become an important tool for analyzing complicated phenomena in the real world. This approach has a wide range of applications in the fields of biology, medicine, environment and engineering. Based on the characteristics of the Exponentiated Kumaraswamy model, researchers have carried out a series of explorations and studies. However, in order to provide a more appropriate model to support real-life applications and to make our data analysis more reliable, it is still meaningful to explore new models. Therefore, in this thesis, we propose a novel mixture model, which is a modified one based on the combination of the Type-II Half Logistic distribution and the Exponentiated Kumaraswamy distribution. We defined it as the Modified Half Logistic - Exponentiated Kumaraswamy (MHL-EK) distribution. Then, we will study its statistical properties, such as quantile function, moments, incomplete moments, etc. Next, maximum likelihood estimation will be used for the parameter estimation of the MHL-EK model. We will also present a related simulation study. Finally, we apply this model to three real data sets and give our analysis for them. The results show that the MHL-EK distribution is a relatively suitable model among the candidate models.","abstract_has_math":false,"creators":["Xiao, Mei"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Science (MSc)","degree_level":"Master's","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":[],"advisors":["Volodin, Andrei"],"committee_chairs":[],"committee_members":["Deng, Dianliang"],"year":2022,"date_issued":"2022-11","date_published":"2022-11","updated_at":"2026-07-24T04:03:45Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4801"],"render_values":[{"text":"https://doi.org/10.82465/4801","href":"https://doi.org/10.82465/4801","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/16166","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Volodin, Andrei"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Deng, Dianliang"]},{"key":"dc:creator","label":"Author","values":["Xiao, Mei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-11-22T22:07:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-11-22T22:07:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-11"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4801"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/16166"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Statistics, University of Regina. xii, 97 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["The statistical model of mixture distributions has become an important tool for analyzing complicated phenomena in the real world. This approach has a wide range of applications in the fields of biology, medicine, environment and engineering. Based on the characteristics of the Exponentiated Kumaraswamy model, researchers have carried out a series of explorations and studies. However, in order to provide a more appropriate model to support real-life applications and to make our data analysis more reliable, it is still meaningful to explore new models. Therefore, in this thesis, we propose a novel mixture model, which is a modified one based on the combination of the Type-II Half Logistic distribution and the Exponentiated Kumaraswamy distribution. We defined it as the Modified Half Logistic - Exponentiated Kumaraswamy (MHL-EK) distribution. Then, we will study its statistical properties, such as quantile function, moments, incomplete moments, etc. Next, maximum likelihood estimation will be used for the parameter estimation of the MHL-EK model. We will also present a related simulation study. Finally, we apply this model to three real data sets and give our analysis for them. The results show that the MHL-EK distribution is a relatively suitable model among the candidate models."]},{"key":"dc:title","label":"Title","values":["Modified half logistic - exponentiated Kumaraswamy distribution and applications"]}]}],"canonical_facts":{"dc:contributor.advisor":["Volodin, Andrei"],"dc:contributor.committeemember":["Deng, Dianliang"],"dc:creator":["Xiao, Mei"],"dc:date.accessioned":["2023-11-22T22:07:48Z"],"dc:date.available":["2023-11-22T22:07:48Z"],"dc:date.issued":["2022-11"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Statistics, University of Regina. xii, 97 p."],"dc:description.abstract":["The statistical model of mixture distributions has become an important tool for analyzing complicated phenomena in the real world. This approach has a wide range of applications in the fields of biology, medicine, environment and engineering. Based on the characteristics of the Exponentiated Kumaraswamy model, researchers have carried out a series of explorations and studies. However, in order to provide a more appropriate model to support real-life applications and to make our data analysis more reliable, it is still meaningful to explore new models. Therefore, in this thesis, we propose a novel mixture model, which is a modified one based on the combination of the Type-II Half Logistic distribution and the Exponentiated Kumaraswamy distribution. We defined it as the Modified Half Logistic - Exponentiated Kumaraswamy (MHL-EK) distribution. Then, we will study its statistical properties, such as quantile function, moments, incomplete moments, etc. Next, maximum likelihood estimation will be used for the parameter estimation of the MHL-EK model. We will also present a related simulation study. Finally, we apply this model to three real data sets and give our analysis for them. The results show that the MHL-EK distribution is a relatively suitable model among the candidate models."],"dc:identifier.doi":["https://doi.org/10.82465/4801"],"dc:identifier.uri":["https://hdl.handle.net/10294/16166"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Modified half logistic - exponentiated Kumaraswamy distribution and applications"],"dc:type":["master thesis"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:45Z"}