A new class of distribution called the generalized Lindley-Weibull distribution for modeling lifetime data is proposed. This model further generalizes the Lindley distribution and allows for hazard rate functions that are monotonically decreasing, monotonically increasing bathtub and upside down bathtub shaped. The model provides a better fit to data in the sense that it leads to more accurate results and prediction, which should facilitate better public policy in a wide range of areas including but not limited to medicine and environmental health, genetics, reliability, survival analysis and time-to event data analysis. A comprehensive investigation and account of the mathematical and statistical properties and those of its sub models including estimation, and simulation issues are presented. Entropy which measures the variation of the uncertainty in a model and Fisher information are presented. Estimates of model parameters are obtained and some applications as well as numerical examples given. Estimates of sub models parameters are also determined from samples with type I right and type II doubly censored data. Real data examples are presented to illustrate the usefulness of these class of distributions.
","abstract_html":"<p>A new class of distribution called the generalized Lindley-Weibull distribution for modeling lifetime data is proposed. This model further generalizes the Lindley distribution and allows for hazard rate functions that are monotonically decreasing, monotonically increasing bathtub and upside down bathtub shaped. The model provides a better fit to data in the sense that it leads to more accurate results and prediction, which should facilitate better public policy in a wide range of areas including but not limited to medicine and environmental health, genetics, reliability, survival analysis and time-to event data analysis. A comprehensive investigation and account of the mathematical and statistical properties and those of its sub models including estimation, and simulation issues are presented. Entropy which measures the variation of the uncertainty in a model and Fisher information are presented. Estimates of model parameters are obtained and some applications as well as numerical examples given. Estimates of sub models parameters are also determined from samples with type I right and type II doubly censored data. Real data examples are presented to illustrate the usefulness of these class of distributions.</p>","abstract_has_math":false,"creators":["Mutiso, Fedelis"],"institution":null,"degree_name":"Master of Science in Mathematics (M.S.)","degree_level":"Thesis (restricted to Georgia Southern)","degree_discipline":"Department of Mathematical Sciences","degree_department":null,"school":null,"contributors":["Charles Champ","Hani Samawi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-01-01T08:00:00Z","date_published":"2013-01-01T08:00:00Z","updated_at":"2026-07-24T02:27:41Z","subjects":["ETD","Transformed Distributions","Generalized Lindley-Weibull Distribution","Related Distributions","Applied Statistics","Biology","Biostatistics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/843","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Charles Champ","Hani Samawi"]},{"key":"dc:creator","label":"Author","values":["Mutiso, Fedelis"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2013-09-17T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Mathematical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (restricted to Georgia Southern)"]},{"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","Transformed Distributions","Generalized Lindley-Weibull Distribution","Related Distributions","Applied Statistics","Biology","Biostatistics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/843"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A new class of distribution called the generalized Lindley-Weibull distribution for modeling lifetime data is proposed. This model further generalizes the Lindley distribution and allows for hazard rate functions that are monotonically decreasing, monotonically increasing bathtub and upside down bathtub shaped. The model provides a better fit to data in the sense that it leads to more accurate results and prediction, which should facilitate better public policy in a wide range of areas including but not limited to medicine and environmental health, genetics, reliability, survival analysis and time-to event data analysis. A comprehensive investigation and account of the mathematical and statistical properties and those of its sub models including estimation, and simulation issues are presented. Entropy which measures the variation of the uncertainty in a model and Fisher information are presented. Estimates of model parameters are obtained and some applications as well as numerical examples given. Estimates of sub models parameters are also determined from samples with type I right and type II doubly censored data. Real data examples are presented to illustrate the usefulness of these class of distributions.
"]},{"key":"dc:title","label":"Title","values":["The Generalized Lindley-Weibull Distribution with Applications to Lifetime Data"]}]}],"canonical_facts":{"dc:contributor":["Charles Champ","Hani Samawi"],"dc:creator":["Mutiso, Fedelis"],"dc:date.available":["2013-09-17T07:00:00Z"],"dc:description.abstract":["A new class of distribution called the generalized Lindley-Weibull distribution for modeling lifetime data is proposed. This model further generalizes the Lindley distribution and allows for hazard rate functions that are monotonically decreasing, monotonically increasing bathtub and upside down bathtub shaped. The model provides a better fit to data in the sense that it leads to more accurate results and prediction, which should facilitate better public policy in a wide range of areas including but not limited to medicine and environmental health, genetics, reliability, survival analysis and time-to event data analysis. A comprehensive investigation and account of the mathematical and statistical properties and those of its sub models including estimation, and simulation issues are presented. Entropy which measures the variation of the uncertainty in a model and Fisher information are presented. Estimates of model parameters are obtained and some applications as well as numerical examples given. Estimates of sub models parameters are also determined from samples with type I right and type II doubly censored data. Real data examples are presented to illustrate the usefulness of these class of distributions.
"],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/843"],"dc:subject":["ETD","Transformed Distributions","Generalized Lindley-Weibull Distribution","Related Distributions","Applied Statistics","Biology","Biostatistics"],"dc:title":["The Generalized Lindley-Weibull Distribution with Applications to Lifetime Data"],"thesis:degree_discipline":["Department of Mathematical Sciences"],"thesis:degree_level":["Thesis (restricted to Georgia Southern)"],"thesis:degree_name":["Master of Science in Mathematics (M.S.)"]},"updated_at":"2026-07-24T02:27:41Z"}