Woods, D. C.
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.
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.
Conference or Workshop Item
|Venue - Dates:
||Conference on New Directions in Experimental Design DAE 2003, 2003-05-16 - 2003-05-18
|17 May 2003||Published|
||06 Jun 2005
||16 Apr 2017 23:27
|Further Information:||Google Scholar|
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