{"id":{"repo_id":"unlv","oai_identifier":"oai:oasis.library.unlv.edu:rtds-2430"},"canonical_url":"https://search.dev.ndltd.org/etd/unlv/oai:oasis.library.unlv.edu:rtds-2430","repository":{"repo_id":"unlv","name":"University of Nevada - Las Vegas","base_url":"https://oasis.library.unlv.edu/do/oai/"},"display":{"title":"Confidence intervals for exposure risk assessment","abstract":"The problem of estimating exposure to a pollutant involves estimation of chemical intake Q. Typically, Q has the form Q = q11q12&cdots;q 1r1/q211q22 &cdots;q2r2t where all thetaij's are unknown means of certain random variables. In assessing the risk to human health from exposure to the pollutant of concern, a confidence interval estimate of Q is needed. In the case of independent normal random variables, point estimates and certain types of interval estimates of a product of several parameters exist in the literature, but not for a ratio of products. In many situations, lot of prior knowledge is available regarding the random variables corresponding to these parameters. In the case of independent normal random variables, we look at this problem from a Bayesian approach, and show how to incorporate prior knowledge to calculate confidence interval for Q. In situations with no prior knowledge, generalized Bayes approach with noninformative priors can be used. Several real and simulated examples are provided to demonstrate the proposed procedures.","abstract_html":"The problem of estimating exposure to a pollutant involves estimation of chemical intake Q. Typically, Q has the form Q = q11q12&cdots;q 1r1/q211q22 &cdots;q2r2t where all thetaij's are unknown means of certain random variables. In assessing the risk to human health from exposure to the pollutant of concern, a confidence interval estimate of Q is needed. In the case of independent normal random variables, point estimates and certain types of interval estimates of a product of several parameters exist in the literature, but not for a ratio of products. In many situations, lot of prior knowledge is available regarding the random variables corresponding to these parameters. In the case of independent normal random variables, we look at this problem from a Bayesian approach, and show how to incorporate prior knowledge to calculate confidence interval for Q. In situations with no prior knowledge, generalized Bayes approach with noninformative priors can be used. Several real and simulated examples are provided to demonstrate the proposed procedures.","abstract_has_math":false,"creators":["Kirkham, Mark S"],"institution":"University of Nevada, Las Vegas","degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Mathematical Sciences","degree_department":null,"school":null,"contributors":["Malwane Ananda"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2002,"date_issued":"2002-01-01T08:00:00Z","date_published":"2002-01-01T08:00:00Z","updated_at":"2026-07-24T05:25:33Z","subjects":[],"languages":["English"],"rights":["IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://oasis.library.unlv.edu/rtds/1431"],"render_values":[{"text":"https://oasis.library.unlv.edu/rtds/1431","href":"https://oasis.library.unlv.edu/rtds/1431","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25669/gzag-edmj","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Malwane Ananda"]},{"key":"dc:creator","label":"Author","values":["Kirkham, Mark S"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["University of Nevada, Las Vegas"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["IN COPYRIGHT. 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In the case of independent normal random variables, point estimates and certain types of interval estimates of a product of several parameters exist in the literature, but not for a ratio of products. In many situations, lot of prior knowledge is available regarding the random variables corresponding to these parameters. In the case of independent normal random variables, we look at this problem from a Bayesian approach, and show how to incorporate prior knowledge to calculate confidence interval for Q. In situations with no prior knowledge, generalized Bayes approach with noninformative priors can be used. Several real and simulated examples are provided to demonstrate the proposed procedures."]},{"key":"dc:format","label":"Dc Format","values":["pdf"]},{"key":"dc:title","label":"Title","values":["Confidence intervals for exposure risk assessment"]}]}],"canonical_facts":{"dc:contributor":["Malwane Ananda"],"dc:creator":["Kirkham, Mark S"],"dc:description.abstract":["The problem of estimating exposure to a pollutant involves estimation of chemical intake Q. Typically, Q has the form Q = q11q12&cdots;q 1r1/q211q22 &cdots;q2r2t where all thetaij's are unknown means of certain random variables. In assessing the risk to human health from exposure to the pollutant of concern, a confidence interval estimate of Q is needed. In the case of independent normal random variables, point estimates and certain types of interval estimates of a product of several parameters exist in the literature, but not for a ratio of products. In many situations, lot of prior knowledge is available regarding the random variables corresponding to these parameters. In the case of independent normal random variables, we look at this problem from a Bayesian approach, and show how to incorporate prior knowledge to calculate confidence interval for Q. In situations with no prior knowledge, generalized Bayes approach with noninformative priors can be used. Several real and simulated examples are provided to demonstrate the proposed procedures."],"dc:format":["pdf"],"dc:identifier":["10.25669/gzag-edmj","https://oasis.library.unlv.edu/rtds/1431","https://oasis.library.unlv.edu/context/rtds/article/2430/viewcontent/uc.pdf"],"dc:language":["English"],"dc:publisher":["University of Nevada, Las Vegas"],"dc:rights":["IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/"],"dc:title":["Confidence intervals for exposure risk assessment"],"dc:type":["Text"],"thesis:degree_discipline":["Mathematical Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:25:33Z"}