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Minimum bias designs under random contamination: application to polynomial spline models

Record type: Conference or Workshop Item (Other)

Minimising the bias due to model misspecification has long been a method for choosing designs. An approach is presented that incorporates prior knowledge of the possible form of the true model via an additive contamination term, regarded as a realisation of a random variable. This induces a random bias term for any given design. A prior distribution for the contamination is obtained either directly or from prior distributions for the individual elements of the contamination. A search technique is used to find designs, where properties of the bias distribution are estimated by simulation.
Several different criteria for choosing a design are proposed, motivated by the distribution of the bias. These criteria are investigated for models where the contamination has a polynomial spline form with uncertainty in the number of knots and their locations. The sensitivity of the resulting designs to the prior distributions is examined.

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Citation

Woods, D. C. (2003) Minimum bias designs under random contamination: application to polynomial spline models At Conference on New Directions in Experimental Design DAE 2003. 16 - 18 May 2003.

More information

Published date: 17 May 2003
Venue - Dates: Conference on New Directions in Experimental Design DAE 2003, 2003-05-16 - 2003-05-18
Organisations: Statistics

Identifiers

Local EPrints ID: 15835
URI: http://eprints.soton.ac.uk/id/eprint/15835
PURE UUID: 4057095d-25f1-4976-9cbb-32ee4a5fa1b0

Catalogue record

Date deposited: 06 Jun 2005
Last modified: 17 Jul 2017 16:46

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