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Algorithm-based distribution of two-sample statistics

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Abstract

In this article, we develop an algorithm for working out the distribution of proposed test statistics. The computed distribution is compared with its existing asymptotic normal approximation using convergence rates and disparity measures. Simulation study is made to estimate the level of significance, and power to assess the performance of algorithm based procedure in comparison to asymptotic normal approximation. Both power comparisons and convergence rates suggest that the distribution computed through algorithm procedure be used for smaller sample sizes.

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Authors and Affiliations

  1. Department of Statistics, Panjab University, 160014, Chandigarh, India

    Narinder Kumar

  2. Department of Mathematics, Panjab University, Chandigarh, India

    Manesh Chawla

Authors
  1. Narinder Kumar
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  2. Manesh Chawla
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Correspondence to Narinder Kumar.

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Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ujsp.

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Kumar, N., Chawla, M. Algorithm-based distribution of two-sample statistics. J Stat Theory Pract 10, 563–573 (2016). https://doi.org/10.1080/15598608.2016.1191046

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  • DOI: https://doi.org/10.1080/15598608.2016.1191046

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