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Parametric Robust Test for Several Variances with Unknown Underlying Distributions

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Abstract

Tsou (in comm Stat-Theor Math 32: 2013–2019, 2003) proposed a parametric robust procedure for testing the equality of two population variances. With large samples the proposed test remains valid under model misspecification. In this article the robust technique is further extended to the comparison of several population variances. More specifically the score test derived on the basis of normal models is adjusted to become robust. The adjusted robust test provides asymptotically valid inference so long as the true underlying distributions have finite fourth moments. Unlike most robust nonparametric approaches, this novel robust technique too provides legitimate variance estimates for estimators of the interested parameters.

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Authors and Affiliations

  1. Institute of Statistics, Institute of System Biology and Bioinformatics, National Central University, No. 300, Jhongda Road, Jhongli City, 32054, Taiwan

    Tsung-Shan Tsou

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  1. Tsung-Shan Tsou
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Correspondence to Tsung-Shan Tsou.

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Tsou, TS. Parametric Robust Test for Several Variances with Unknown Underlying Distributions. Metrika 64, 333–349 (2006). https://doi.org/10.1007/s00184-006-0052-0

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  • DOI: https://doi.org/10.1007/s00184-006-0052-0

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