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A New Class of Robust Two-Sample Wald-Type Tests

Author

Listed:
  • Ghosh Abhik
  • Basu Ayanendranath

    (Kolkata Interdisciplinary Statistical Research Unit 203, Indian Statistical Institute, B. T. Road, Kolkata- 700108, India)

  • Martin Nirian

    (Departamento de Estadistica e I.O., Complutense University of Madrid, II Avenida de Islas Filipinas 3, Madrid28003, Spain)

  • Pardo Leandro

    (Departamento de Estadistica e I.O., Complutense University of Madrid, Plaza de Ciencias 3, Madrid28040, Spain)

Abstract

Parametric hypothesis testing associated with two independent samples arises frequently in several applications in biology, medical sciences, epidemiology, reliability and many more. In this paper, we propose robust Wald-type tests for testing such two sample problems using the minimum density power divergence estimators of the underlying parameters. In particular, we consider the simple two-sample hypothesis concerning the full parametric homogeneity as well as the general two-sample (composite) hypotheses involving some nuisance parameters. The asymptotic and theoretical robustness properties of the proposed Wald-type tests have been developed for both the simple and general composite hypotheses. Some particular cases of testing against one-sided alternatives are discussed with specific attention to testing the effectiveness of a treatment in clinical trials. Performances of the proposed tests have also been illustrated numerically through appropriate real data examples.

Suggested Citation

  • Ghosh Abhik & Basu Ayanendranath & Martin Nirian & Pardo Leandro, 2018. "A New Class of Robust Two-Sample Wald-Type Tests," The International Journal of Biostatistics, De Gruyter, vol. 14(2), pages 1-29, November.
    • Handle: RePEc:bpj:ijbist:v:14:y:2018:i:2:p:29:n:1
    DOI: 10.1515/ijb-2017-0023
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    References listed on IDEAS

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    1. Toma, Aida & Broniatowski, Michel, 2011. "Dual divergence estimators and tests: Robustness results," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 20-36, January.
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