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Test for generalized variance in signal processing

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  • Bhandary, Madhusudan

Abstract

We derive a likelihood ratio test for generalized variance in signal processing following the method of Sengupta (1987). We discuss the solution in both white noise and colored noise cases. The asymptotic distribution of the test statistics in both the cases follows chi-square with one degree of freedom from general theory of likelihood ratio test.

Suggested Citation

  • Bhandary, Madhusudan, 1996. "Test for generalized variance in signal processing," Statistics & Probability Letters, Elsevier, vol. 27(2), pages 155-162, April.
    • Handle: RePEc:eee:stapro:v:27:y:1996:i:2:p:155-162
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    References listed on IDEAS

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    1. SenGupta, Ashis, 1987. "Tests for standardized generalized variances of multivariate normal populations of possibly different dimensions," Journal of Multivariate Analysis, Elsevier, vol. 23(2), pages 209-219, December.
    2. Zhao, L. C. & Krishnaiah, P. R. & Bai, Z. D., 1986. "On detection of the number of signals when the noise covariance matrix is arbitrary," Journal of Multivariate Analysis, Elsevier, vol. 20(1), pages 26-49, October.
    3. Krishnaiah, P. R., 1976. "Some recent developments on complex multivariate distributions," Journal of Multivariate Analysis, Elsevier, vol. 6(1), pages 1-30, March.
    4. Zhao, L. C. & Krishnaiah, P. R. & Bai, Z. D., 1986. "On detection of the number of signals in presence of white noise," Journal of Multivariate Analysis, Elsevier, vol. 20(1), pages 1-25, October.
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    Cited by:

    1. Ashis SenGupta & Hon Keung Tony Ng, 2011. "Nonparametric test for the homogeneity of the overall variability," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1751-1768, September.
    2. Dariush Najarzadeh, 2019. "Testing equality of standardized generalized variances of k multivariate normal populations with arbitrary dimensions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 593-623, December.

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