{"id":{"repo_id":"mo-state","oai_identifier":"oai:bearworks.missouristate.edu:theses-2022"},"canonical_url":"https://search.dev.ndltd.org/etd/mo-state/oai:bearworks.missouristate.edu:theses-2022","repository":{"repo_id":"mo-state","name":"Missouri State University","base_url":"https://bearworks.missouristate.edu/do/oai/"},"display":{"title":"Bias Reduction of Estimates By Bootstrap Method","abstract":"In general it is desirable to have unbiased estimators for parameters of a probability distribution function. However, there are several estimators which are not unbiased. In this thesis, we show by direct computation that the bias of the bootstrap estimate of μ⁴ can be reduced, where μ is the mean of the population. We consider both nonparametric and parametric cases. In the parametric case, the bias of the bootstrap estimate is computed for normal, exponential and Poisson distributions.","abstract_html":"In general it is desirable to have unbiased estimators for parameters of a probability distribution function. However, there are several estimators which are not unbiased. In this thesis, we show by direct computation that the bias of the bootstrap estimate of μ⁴ can be reduced, where μ is the mean of the population. We consider both nonparametric and parametric cases. In the parametric case, the bias of the bootstrap estimate is computed for normal, exponential and Poisson distributions.","abstract_has_math":false,"creators":["Schuchman, Linda J."],"institution":null,"degree_name":"Master of Science in Mathematics","degree_level":"Masters","degree_discipline":"Mathematics","degree_department":null,"school":null,"contributors":["George Mathew"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1995,"date_issued":"1995-05-01T07:00:00Z","date_published":"1995-05-01T07:00:00Z","updated_at":"2026-07-24T03:16:06Z","subjects":["Mathematics"],"languages":[],"rights":["© Linda J. Schuchman"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://bearworks.missouristate.edu/theses/1021","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["George Mathew"]},{"key":"dc:creator","label":"Author","values":["Schuchman, Linda J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mathematics"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© Linda J. Schuchman"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://bearworks.missouristate.edu/theses/1021"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In general it is desirable to have unbiased estimators for parameters of a probability distribution function. However, there are several estimators which are not unbiased. In this thesis, we show by direct computation that the bias of the bootstrap estimate of μ⁴ can be reduced, where μ is the mean of the population. We consider both nonparametric and parametric cases. In the parametric case, the bias of the bootstrap estimate is computed for normal, exponential and Poisson distributions."]},{"key":"dc:title","label":"Title","values":["Bias Reduction of Estimates By Bootstrap Method"]}]}],"canonical_facts":{"dc:contributor":["George Mathew"],"dc:creator":["Schuchman, Linda J."],"dc:description.abstract":["In general it is desirable to have unbiased estimators for parameters of a probability distribution function. However, there are several estimators which are not unbiased. In this thesis, we show by direct computation that the bias of the bootstrap estimate of μ⁴ can be reduced, where μ is the mean of the population. We consider both nonparametric and parametric cases. In the parametric case, the bias of the bootstrap estimate is computed for normal, exponential and Poisson distributions."],"dc:identifier":["https://bearworks.missouristate.edu/theses/1021"],"dc:rights":["© Linda J. Schuchman"],"dc:subject":["Mathematics"],"dc:title":["Bias Reduction of Estimates By Bootstrap Method"],"thesis:degree_discipline":["Mathematics"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science in Mathematics"]},"updated_at":"2026-07-24T03:16:06Z"}