{"id":{"repo_id":"must-thes","oai_identifier":"oai:scholarsmine.mst.edu:doctoral_dissertations-4179"},"canonical_url":"https://search.dev.ndltd.org/etd/must-thes/oai:scholarsmine.mst.edu:doctoral_dissertations-4179","repository":{"repo_id":"must-thes","name":"Missouri University of Science and Technology","base_url":"https://scholarsmine.mst.edu/do/oai/"},"display":{"title":"Semiparametric estimation with clustered right censored data via multivariate gaussian random fields","abstract":"Consider a fixed number of clustered areas identified by their geographical coordinate that are monitored for the occurrences of an event such as pandemic, epidemic, migration to name a few. Data collected on units at all areas include time varying covariates and other environmental factors that may affect event occurrences. The event times in every area can be independent. They can also be correlated with correlation between two units induced by an unobservable frailty. In both cases, the collected data is considered pairwise to account for spatial correlation between all pair of areas. The pairwise right censored data is probit-transformed yielding a multivariate Gaussian random field preserving the spatial correlation function. The data is analyzed using counting process and geostatistical formulation that led to a class of weighted pairwise semiparametric estimating functions. In the independence case, estimators of models unknowns are shown to be consistent and asymptotically normally distributed under infill-type spatial statistics asymptotic. Detailed small sample numerical studies that are in agreement with the theoretical results are provided in the independence case. In the dependence case, the estimators are shown to be inefficiency when the dependence is ignored. The foregoing procedures are applied to Leukemia survival data in Northeast England--Abstract, page iv.","abstract_html":"Consider a fixed number of clustered areas identified by their geographical coordinate that are monitored for the occurrences of an event such as pandemic, epidemic, migration to name a few. Data collected on units at all areas include time varying covariates and other environmental factors that may affect event occurrences. The event times in every area can be independent. They can also be correlated with correlation between two units induced by an unobservable frailty. In both cases, the collected data is considered pairwise to account for spatial correlation between all pair of areas. The pairwise right censored data is probit-transformed yielding a multivariate Gaussian random field preserving the spatial correlation function. The data is analyzed using counting process and geostatistical formulation that led to a class of weighted pairwise semiparametric estimating functions. In the independence case, estimators of models unknowns are shown to be consistent and asymptotically normally distributed under infill-type spatial statistics asymptotic. Detailed small sample numerical studies that are in agreement with the theoretical results are provided in the independence case. In the dependence case, the estimators are shown to be inefficiency when the dependence is ignored. The foregoing procedures are applied to Leukemia survival data in Northeast England--Abstract, page iv.","abstract_has_math":false,"creators":["Sainul Abdeen, Fathima Zahra"],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Mathematics","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T03:18:09Z","subjects":["Clustered failure times","Composite likelihood","Estimating function","Gaussian random fields","Infill asymptoticSpatial correlation","Statistics and Probability"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/3174","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Sainul Abdeen, Fathima Zahra"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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Data collected on units at all areas include time varying covariates and other environmental factors that may affect event occurrences. The event times in every area can be independent. They can also be correlated with correlation between two units induced by an unobservable frailty. In both cases, the collected data is considered pairwise to account for spatial correlation between all pair of areas. The pairwise right censored data is probit-transformed yielding a multivariate Gaussian random field preserving the spatial correlation function. The data is analyzed using counting process and geostatistical formulation that led to a class of weighted pairwise semiparametric estimating functions. In the independence case, estimators of models unknowns are shown to be consistent and asymptotically normally distributed under infill-type spatial statistics asymptotic. Detailed small sample numerical studies that are in agreement with the theoretical results are provided in the independence case. In the dependence case, the estimators are shown to be inefficiency when the dependence is ignored. The foregoing procedures are applied to Leukemia survival data in Northeast England--Abstract, page iv."]},{"key":"dc:title","label":"Title","values":["Semiparametric estimation with clustered right censored data via multivariate gaussian random fields"]}]}],"canonical_facts":{"dc:creator":["Sainul Abdeen, Fathima Zahra"],"dc:description.abstract":["Consider a fixed number of clustered areas identified by their geographical coordinate that are monitored for the occurrences of an event such as pandemic, epidemic, migration to name a few. Data collected on units at all areas include time varying covariates and other environmental factors that may affect event occurrences. The event times in every area can be independent. They can also be correlated with correlation between two units induced by an unobservable frailty. In both cases, the collected data is considered pairwise to account for spatial correlation between all pair of areas. The pairwise right censored data is probit-transformed yielding a multivariate Gaussian random field preserving the spatial correlation function. The data is analyzed using counting process and geostatistical formulation that led to a class of weighted pairwise semiparametric estimating functions. In the independence case, estimators of models unknowns are shown to be consistent and asymptotically normally distributed under infill-type spatial statistics asymptotic. Detailed small sample numerical studies that are in agreement with the theoretical results are provided in the independence case. In the dependence case, the estimators are shown to be inefficiency when the dependence is ignored. The foregoing procedures are applied to Leukemia survival data in Northeast England--Abstract, page iv."],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/3174"],"dc:subject":["Clustered failure times","Composite likelihood","Estimating function","Gaussian random fields","Infill asymptoticSpatial correlation","Statistics and Probability"],"dc:title":["Semiparametric estimation with clustered right censored data via multivariate gaussian random fields"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. D. in Mathematics"],"thesis:institution_name":["Missouri University of Science and Technology"]},"updated_at":"2026-07-24T03:18:09Z"}