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Screening among Multivariate Normal Data

Author

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  • Chen, Pinyuen
  • Melvin, William L.
  • Wicks, Michael C.

Abstract

This paper considers the problem of screeningkmultivariate normal populations (secondary data) with respect to a control population (primary data) in terms of covariance structure. A screening procedure, developed based upon statistical ranking and selection theory, is designed to include in the selected subset those populations which have the same (or similar) covariance structure as the control population, and exclude those populations which differ significantly. Formulas for computing the probability of a correct selection and the least favorable configuration are developed. The sample size required to achieve a specific probability requirement is also developed, with results presented in tabular form. This secondary data selection procedure is illustrated via an example with applications to radar signal processing.

Suggested Citation

  • Chen, Pinyuen & Melvin, William L. & Wicks, Michael C., 1999. "Screening among Multivariate Normal Data," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 10-29, April.
    • Handle: RePEc:eee:jmvana:v:69:y:1999:i:1:p:10-29
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    References listed on IDEAS

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    1. Khatri, C. G., 1979. "Characterizations of multivariate normality II. Through linear regressions," Journal of Multivariate Analysis, Elsevier, vol. 9(4), pages 589-598, December.
    2. Khatri, C. G. & Rao, C. Radhakrishna, 1987. "Test for a specified signal when the noise covariance matrix is unknown," Journal of Multivariate Analysis, Elsevier, vol. 22(2), pages 177-188, August.
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    Cited by:

    1. Mady, Afaf M., 2006. "Some extensions of Langenberg model for clinical trials with delayed observations normally distributed responses," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1384-1392, November.

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