Quantifying uncertainty in the estimation of probability distributions
We consider ordinary least squares parameter estimation problemswhere the unknown parameters to be estimated are probabilitydistributions. A computational framework for quantification ofuncertainty (e.g., standard errors) associated with the estimatedparameters is given and sample numerical findings...
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| Format: | Article |
| Language: | English |
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AIMS Press
2008-09-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2008.5.647 |
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| author | H.T. Banks Jimena L. Davis |
| author_facet | H.T. Banks Jimena L. Davis |
| author_sort | H.T. Banks |
| collection | DOAJ |
| description | We consider ordinary least squares parameter estimation problemswhere the unknown parameters to be estimated are probabilitydistributions. A computational framework for quantification ofuncertainty (e.g., standard errors) associated with the estimatedparameters is given and sample numerical findings are presented. |
| format | Article |
| id | doaj-art-246f648824964d858862f8d827dc5a69 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2008-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-246f648824964d858862f8d827dc5a692025-01-24T01:58:42ZengAIMS PressMathematical Biosciences and Engineering1551-00182008-09-015464766710.3934/mbe.2008.5.647Quantifying uncertainty in the estimation of probability distributionsH.T. Banks0Jimena L. Davis1Center for Research in Scientific Computation, North Carolina State University, Raleigh, North Carolina 27695-8205Center for Research in Scientific Computation, North Carolina State University, Raleigh, North Carolina 27695-8205We consider ordinary least squares parameter estimation problemswhere the unknown parameters to be estimated are probabilitydistributions. A computational framework for quantification ofuncertainty (e.g., standard errors) associated with the estimatedparameters is given and sample numerical findings are presented.https://www.aimspress.com/article/doi/10.3934/mbe.2008.5.647parameter estimationasymptotic standard error theoryconfidence bandsprobability distributionsapproximationsize-structured populations |
| spellingShingle | H.T. Banks Jimena L. Davis Quantifying uncertainty in the estimation of probability distributions Mathematical Biosciences and Engineering parameter estimation asymptotic standard error theory confidence bands probability distributions approximation size-structured populations |
| title | Quantifying uncertainty in the estimation of probability distributions |
| title_full | Quantifying uncertainty in the estimation of probability distributions |
| title_fullStr | Quantifying uncertainty in the estimation of probability distributions |
| title_full_unstemmed | Quantifying uncertainty in the estimation of probability distributions |
| title_short | Quantifying uncertainty in the estimation of probability distributions |
| title_sort | quantifying uncertainty in the estimation of probability distributions |
| topic | parameter estimation asymptotic standard error theory confidence bands probability distributions approximation size-structured populations |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2008.5.647 |
| work_keys_str_mv | AT htbanks quantifyinguncertaintyintheestimationofprobabilitydistributions AT jimenaldavis quantifyinguncertaintyintheestimationofprobabilitydistributions |