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


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, Chicago, USA, 16 - 18 May 2003.

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Description/Abstract

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.

Item Type: Conference or Workshop Item (Speech)
Subjects: Q Science > QA Mathematics
H Social Sciences > HA Statistics
Divisions: University Structure - Pre August 2011 > School of Mathematics
University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
University Structure - Pre August 2011 > School of Mathematics > Statistics
ePrint ID: 15835
Date Deposited: 06 Jun 2005
Last Modified: 27 Mar 2014 18:06
URI: http://eprints.soton.ac.uk/id/eprint/15835

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