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Inference for the Behrens-Fisher problem after outlier removal

  • MU Weiyan ,
  • WANG Junhao ,
  • XIONG Shifeng
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  • 1 School of Sciences, Beijing University of Civil Engineering and Architecture, Beijing 102616, China;
    2 School of Mathematical Sciences, University of Chinese Academy and Science, Beijing 101408, China;
    3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Online published: 2025-04-16

Abstract

Selective inference aims at reducing the bias caused by the detect-and-forget strategy for handling statistical inferential problems in the presence of outliers. In this paper, we study selective inference for the classical Behrens-Fisher problem. Several selective confidence intervals for the difference between the means of two normal populations with unequal variances are presented, including the selective modifications of the traditional Welch’s interval and several generalized pivotal quantities-based intervals. These methods are compared via Monte Carlo simulations and a real data analysis. Suggestions for practical use are also discussed.

Cite this article

MU Weiyan , WANG Junhao , XIONG Shifeng . Inference for the Behrens-Fisher problem after outlier removal[J]. Journal of University of Chinese Academy of Sciences, 0 : 6 . DOI: 10.7523/j.ucas.2025.018

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