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Asymptotic Properties of the Estimators for Multivariate Components of Variance

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  • Remadi, S.
  • Amemiya, Y.

Abstract

Estimation of the covariance matrices in the multivariate balanced one-way random effect model is discussed. The rank of the between-group covariance matrix plays a large role in model building as well as in assessing asymptotic properties of the estimated covariance matrices. The restricted (residual) maximum likelihood estimators derived under a rank condition are considered. Asymptotic properties of the estimators are derived for a possibly incorrectly specified rank and under either the number of groups, the number of replicates, or both, tending to infinity. A higher order expansion covering various cases leads to a common approximate inference procedure which can be used in a wide range of practical situations. A simulation study is also presented.

Suggested Citation

  • Remadi, S. & Amemiya, Y., 1994. "Asymptotic Properties of the Estimators for Multivariate Components of Variance," Journal of Multivariate Analysis, Elsevier, vol. 49(1), pages 110-131, April.
    • Handle: RePEc:eee:jmvana:v:49:y:1994:i:1:p:110-131
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    Cited by:

    1. Amylou Dueck & Sharon Lohr, 2005. "Robust Estimation of Multivariate Covariance Components," Biometrics, The International Biometric Society, vol. 61(1), pages 162-169, March.
    2. Shin, Chungyeol & Amemiya, Yasuo, 1997. "Algorithms for the likelihood-based estimation of the random coefficient model," Statistics & Probability Letters, Elsevier, vol. 32(2), pages 189-199, March.

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