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

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  • Navinchandra Acharya
  • Harriet Nembhard

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.

Suggested Citation

  • Navinchandra Acharya & Harriet Nembhard, 2009. "Bayesian algorithms for missing observations in experimental designs for a nanolubrication process," IISE Transactions, Taylor & Francis Journals, vol. 41(11), pages 969-978.
    • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:11:p:969-978
    DOI: 10.1080/07408170902806888
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