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Permutation tests from biased samples for the equality of two distributions

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  • Qing Kang
  • Paul Nelson

Abstract

Suppose that independent samples are taken from two distributions according to two biased (nonrandom) sampling plans. This study constructs a class of biased-adjusted permutation tests for the equality of the two distributions. A resampling algorithm that generates permutations with unequal probabilities is proposed to estimate the tests’ exact P-values with a manageable Monte Carlo simulation error. This algorithm leads to the derivation of large-sample, normal theory tests. Conditions under which these tests are consistent are given. The tests’ power at finite sample sizes is examined via a simulation study.

Suggested Citation

  • Qing Kang & Paul Nelson, 2009. "Permutation tests from biased samples for the equality of two distributions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(3), pages 305-319.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:3:p:305-319
    DOI: 10.1080/10485250802617617
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