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Some Point Estimates and Confidence Regions for Multivariate Inter-laboratory Data Analysis

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  • Jian Zhao

    (University of Maryland Baltimore County)

  • Thomas Mathew

    (University of Maryland Baltimore County)

Abstract

The problem of analyzing multivariate data from inter-laboratory studies is considered when the data are modeled using the heteroscedastic multivariate one-way random effects model. The primary problem of interest is inference concerning the common mean vector, and a secondary problem of interest is inference concerning the inter-laboratory variance component. Under the usual multivariate normality assumption, this work investigates the point estimation of the inter-laboratory variance component, and the computation of a confidence region for the common mean vector. Noting that a full likelihood based analysis presents computational challenges, some computationally tractable solutions are presented, and illustrated with an example.

Suggested Citation

  • Jian Zhao & Thomas Mathew, 2018. "Some Point Estimates and Confidence Regions for Multivariate Inter-laboratory Data Analysis," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 147-166, December.
    • Handle: RePEc:spr:sankhb:v:80:y:2018:i:1:d:10.1007_s13571-018-0164-3
    DOI: 10.1007/s13571-018-0164-3
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    References listed on IDEAS

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    1. Mathew, T. & Niyogi, A. & Sinha, B. K., 1994. "Improved Nonnegative Estimation of Variance Components in Balanced Multivariate Mixed Models," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 83-101, October.
    2. Rukhin, Andrew L., 2007. "Estimating common vector parameters in interlaboratory studies," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 435-454, March.
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    Cited by:

    1. Bodnar, Olha & Bodnar, Taras, 2024. "Gibbs sampler approach for objective Bayesian inference in elliptical multivariate meta-analysis random effects model," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).

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