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A test of location for exchangeable multivariate normal data with unknown correlation

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  • Follmann, Dean
  • Proschan, Michael

Abstract

We consider the problem of testing whether the common mean of a single n-vector of multivariate normal random variables with known variance and unknown common correlation [rho] is zero. We derive the standardized likelihood ratio test for known [rho] and explore different ways of proceeding with [rho] unknown. We evaluate the performance of the standardized statistic where [rho] is replaced with an estimate of [rho] and determine the critical value cn that controls the type I error rate for the least favorable [rho] in [0,1]. The constant cn increases with n and this procedure has pathological behavior if [rho] depends on n and [rho]n converges to zero at a certain rate. As an alternate approach, we replace [rho] with the upper limit of a (1-[beta]n) confidence interval chosen so that cn=c for all n. We determine [beta]n so that the type I error rate is exactly controlled for all [rho] in [0,1]. We also investigate a simpler approach where we bound the type I error rate. The former method performs well for all n while the less powerful bound method may be a useful in some settings as a simple approach. The proposed tests can be used in different applications, including within-cluster resampling and combining exchangeable p-values.

Suggested Citation

  • Follmann, Dean & Proschan, Michael, 2012. "A test of location for exchangeable multivariate normal data with unknown correlation," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 115-125, February.
  • Handle: RePEc:eee:jmvana:v:104:y:2012:i:1:p:115-125
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    References listed on IDEAS

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    1. Kost, James T. & McDermott, Michael P., 2002. "Combining dependent P-values," Statistics & Probability Letters, Elsevier, vol. 60(2), pages 183-190, November.
    2. Dean Follmann & Michael Proschan & Eric Leifer, 2003. "Multiple Outputation: Inference for Complex Clustered Data by Averaging Analyses from Independent Data," Biometrics, The International Biometric Society, vol. 59(2), pages 420-429, June.
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    Cited by:

    1. Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Goodness-of-fit test in high-dimensional linear sparse models," LIDAM Discussion Papers CORE 2023008, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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