Bayesian algorithms for missing observations in experimental designs for a nanolubrication process

Navinchandra Acharya and Harriet Nembhard

IISE Transactions, 2009, vol. 41, issue 11, 969-978

Abstract: Three new Bayesian algorithms for missing observations based on predictive ability and minimization of the Residual Sum of Squares (RSS) are proposed. Their performance is compared to three existing algorithms based on an appropriate predicted residual error sum of squares statistic. Different positions of the missing observations and initial model conditions are considered. In all the investigated cases, the Bayesian algorithms perform significantly better than non-Bayesian algorithms. A numerical study is performed using a nanolubrication process. It shows that the Bayesian complete RSS minimization algorithm yields the closest estimates of the missing observations, with the maximum predictive ability.

Date: 2009
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DOI: 10.1080/07408170902806888

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