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Multisample tests for scale based on kernel density estimation

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  • Mizushima, Takamasa

Abstract

We propose test statistics based on kernel density estimation for testing the equality of scale parameters. The statistics are compared with other statistics with respect to the asymptotic relative efficiency. The statistics are more efficient than the c-sample analogs of the two-sample Mood test and the two-sample Ansari-Bradley test for the normal distribution and the Cauchy distribution. We also give a comparison of Type I error and power by simulation.

Suggested Citation

  • Mizushima, Takamasa, 2000. "Multisample tests for scale based on kernel density estimation," Statistics & Probability Letters, Elsevier, vol. 49(1), pages 81-91, August.
    • Handle: RePEc:eee:stapro:v:49:y:2000:i:1:p:81-91
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    References listed on IDEAS

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    1. Hall, Peter & Marron, J. S., 1987. "Estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 6(2), pages 109-115, November.
    2. Jones, M. C. & Sheather, S. J., 1991. "Using non-stochastic terms to advantage in kernel-based estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 11(6), pages 511-514, June.
    3. Ahmad, Ibrahim A. & Li, Qi, 1997. "Testing independence by nonparametric kernel method," Statistics & Probability Letters, Elsevier, vol. 34(2), pages 201-210, June.
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