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Estimating common vector parameters in interlaboratory studies

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  • Rukhin, Andrew L.

Abstract

The primary goal of this work is to extend two methods of random effects models to multiparameter situation. These methods comprise the DerSimonian-Laird estimator, stemming from meta-analysis, and the Mandel-Paule algorithm widely used in interlaboratory studies. The maximum likelihood estimators are also discussed. Two methods of assessing the uncertainty of these estimators are given.

Suggested Citation

  • Rukhin, Andrew L., 2007. "Estimating common vector parameters in interlaboratory studies," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 435-454, March.
  • Handle: RePEc:eee:jmvana:v:98:y:2007:i:3:p:435-454
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    References listed on IDEAS

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    1. Ibrahim J. G. & Chen M-H. & Gray R. J., 2002. "Bayesian Models for Gene Expression With DNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 88-99, March.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    3. Huang, Jian & Wang, Deli & Zhang, Cun-Hui, 2005. "A Two-Way Semilinear Model for Normalization and Analysis of cDNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 814-829, September.
    4. Fan, Jianqing & Peng, Heng & Huang, Tao, 2005. "Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 781-796, September.
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