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Title: | Efficient geometric and uniform design strategies for sigmoidal regression models |
Authors: | Timothy E. O'Brien Suree Chooprateep Nontiya Homkham |
Authors: | Timothy E. O'Brien Suree Chooprateep Nontiya Homkham |
Keywords: | Decision Sciences;Mathematics |
Issue Date: | 15-Jul-2009 |
Abstract: | This paper provides practical guidelines for choosing efficient geometric and uniform designs for the logistic class of dose-response bioassay model functions in both the homoskedastic Gaussian and Binomial settings. The efficiencies of the designs provided here are typically above 90%, and since the number of design support points generally exceeds the number of parameters, these designs provide a useful and efficient means to confirm the assumed model. Extensions of our basic strategy include a Bayesian maxi-min design approach to reflect a range of values of the initial parameter estimates, as well as geometric/uniform design analogues when uncertainty exists as to the correct scale or to take account of curvature. |
URI: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=67650136138&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/59519 |
ISSN: | 0038271X |
Appears in Collections: | CMUL: Journal Articles |
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