Title	Bayesian optimal designs for fitting fractional polynomial response surface models
Creator	Gilmour, Steven
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-08-10T15:31
Description	Fractional polynomial models are potentially useful for response surface investigations. With the availability of routines for fitting nonlinear models in statistical packages they are increasingly being used. However as in all experiments the design should be chosen such that the model parameters are estimated as efficiently as possible. The design choice for such models involves the usual difficulties of nonlinear models design. We find Bayesian optimal exact designs for several fractional factorial models. The optimum designs are compared to various standard designs in response surface problems. Some unusual problems in the choices of prior and optimization method will be noted. (Joint work with Luzia Trinca.)
Extent	43 minutes
Subject	Mathematics; Statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Kings College London
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2018-02-07
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0363437
URI	http://hdl.handle.net/2429/64575
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Bayesian optimal designs for fitting fractional polynomial response surface models Gilmour, Steven

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