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Bayes Linear Sufficiency and Systems of Expert Posterior Assessments
M. Goldstein and A. O'Hagan
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 58, No. 2 (1996), pp. 301-316
Stable URL: http://www.jstor.org/stable/2345978
Page Count: 16
You can always find the topics here!Topics: Cost estimates, Statistical discrepancies, Bayes estimators, Renovations, Statistical estimation, Estimate reliability, Estimators, Mathematical problems, Estimation methods, Random variables
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Data arising in the form of expert assessments are received by a decision maker. The decision maker is required to estimate a set of unknown quantities, and receives expert assessments at varying levels of accuracy, on samples of the quantities of interest. We present a Bayes linear analysis of this problem. In the absence of other assessments, the decision maker will accept as his or her current estimate of any single quantity the most accurate received assessment of that quantity. This leads to a sufficiency property which allows a simple decomposition of the error structure of assessments. Bayes linear estimation is then used by the decision maker to estimate each quantity of interest given an arbitrary collection of received assessments. The analysis is motivated throughout by a practical context in which a large company needs to estimate costs for renovation of assets. The methodology is illustrated with a numerical example.
Journal of the Royal Statistical Society. Series B (Methodological) © 1996 Royal Statistical Society