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On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other

The Annals of Mathematical Statistics(1947)10.1214/aoms/1177730491Source: DataRank Database

On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other is a research paper published in The Annals of Mathematical Statistics (1947). On theSindex it has a DataRank of 1.4. It has been cited 13,652 times. Its calibrated FAIR score is 18/100.

N/A
1.4DataRank · unranked
1.4
Open Access13652 citations · base score 9.5
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Let $x$ and $y$ be two random variables with continuous cumulative distribution functions $f$ and $g$. A statistic $U$ depending on the relative ranks of the $x$'s and $y$'s is proposed for testing the hypothesis $f = g$. Wilcoxon proposed an equivalent test in the Biometrics Bulletin, December, 1945, but gave only a few points of the distribution of his statistic. Under the hypothesis $f = g$ the probability of obtaining a given $U$ in a sample of $n x's$ and $m y's$ is the solution of a certain recurrence relation involving $n$ and $m$. Using this recurrence relation tables have been computed giving the probability of $U$ for samples up to $n = m = 8$. At this point the distribution is almost normal. From the recurrence relation explicit expressions for the mean, variance, and fourth moment are obtained. The 2rth moment is shown to have a certain form which enabled us to prove that the limit distribution is normal if $m, n$ go to infinity in any arbitrary manner. The test is shown to be consistent with respect to the class of alternatives $f(x) > g(x)$ for every $x$.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (0/3)

      FAIR checklist signals are shown for context only and do not affect DataRank scoring.

      18FAIR score
      F Findable
      20
      A Accessible
      30
      I Interoperable
      13
      R Reusable
      8
      Top 98% by FAIRLLM-assessed⚠ abstract only
      Estimated from the abstract only. The agent couldn't read this paper's full text, so body-dependent criteria (data-availability statement, formats, license) are inferred. For a confident score, upload the PDF or supply full text →

      Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      1.4

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      Learn more about DataRank methodology →
      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

      Base score B(p)
      log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
      Network N(p)
      Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
      Damping factor d = 0.85
      DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
      Self-citations excluded
      Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

      Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

      Read the full methodology →

      Authors (3)

      D. R. Whitney,Douglas R. Whitney,H. B. Mann

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