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Optimal Rank Tests

  • Mayer Alvo
  • Philip L. H. Yu
Chapter
Part of the Springer Series in the Data Sciences book series (SSDS)

Abstract

Lehmann and Stein (1949) and Hoeffding (1951b) pioneered the development of an optimal theory for nonparametric tests, parallel to that of Neyman and Pearson (1933) and Wald (1949) for parametric testing. They considered nonparametric hypotheses that are invariant under permutations of the variables in multi-sample problems so that rank statistics are the maximal invariants, and extended the Neyman-Pearson and Wald theories for independent observations to the joint density function of the maximal invariants. Terry (1952) and others subsequently implemented and refined Hoeffding’s approach to show that a number of rank tests are locally most powerful at certain alternatives near the null hypothesis. We shall first consider Hoeffding’s change of measure formula and derive some consequences with respect to the two-sample problem. This formula assumes knowledge of the underlying distribution of the random variables and leads to an optimal choice of score functions and subsequently to locally most powerful tests. Hence, for any given underlying distributions, we may obtain the optimal test statistic.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mayer Alvo
    • 1
  • Philip L. H. Yu
    • 2
  1. 1.Department of Mathematics and StatisticsUniversity of OttawaOttawaCanada
  2. 2.Department of Statistics and Actuarial ScienceUniversity of Hong KongHong KongChina

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