Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design
dc.contributor.author | Jacquez, John A. | en_US |
dc.contributor.author | Greif, Peter | en_US |
dc.date.accessioned | 2006-04-07T18:53:18Z | |
dc.date.available | 2006-04-07T18:53:18Z | |
dc.date.issued | 1985-12 | en_US |
dc.identifier.citation | Jacquez, John A., Greif, Peter (1985/12)."Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design." Mathematical Biosciences 77(1-2): 201-227. <http://hdl.handle.net/2027.42/25472> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6VHX-45GWN5G-2W/2/788827937eae3cff3c8b347043fa216a | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/25472 | |
dc.description.abstract | We define two levels of parameters. The basic parameters are associated with the model and experiment(s). However, the observations define a set of identifiable observational parameters that are functions of the basic parameters. Starting with this formulation, we show that an implicit function approach provides a common basis for examining local identifiability and estimability and gives a lead-in to the problem of optimal sampling design. A least squares approach based on a large but finite set of observations generated at initial parameter estimates then gives a uniform approach to local identifiability, estimability, and the generation of an optimal sampling schedule. | en_US |
dc.format.extent | 1413070 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Natural Resources and Environment | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Ecology and Evolutionary Biology | en_US |
dc.subject.hlbsecondlevel | Biological Chemistry | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Physiology, The University of Michigan, Ann Arbor, Michigan 48109-0010 USA | en_US |
dc.contributor.affiliationother | Laboratory of Mathematical Biology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20205, USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/25472/1/0000012.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0025-5564(85)90098-7 | en_US |
dc.identifier.source | Mathematical Biosciences | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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