Cumulative Distribution Function Comparisons
Cumulative Distribution Function Comparisons is a methodological framework in the theory of stochastic dominance that evaluates and ranks random outcomes by systematically comparing the cumulative distribution functions of probability distributions to determine dominance relationships based on their relative shift and spread.
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2.3K
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109
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74
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2017–2026
11
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A two-dimensional Kolmogorov-Smirnov test
Raul Lopes · 2009 · 119 citations · Full text
Buhong Zheng · Journal of Economic Surveys · 2000 · 91 citations
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Brunel University of London
London, United Kingdom
2 h-index
4 publications
206 citations
Acharya Nagarjuna University
Guntur, India
1 h-index
2 publications
42 citations
Philipps University of Marburg
Marburg, Germany
1 h-index
3 publications
42 citations

University of Padua
Padua, Italy
1 h-index
1 publications
14 citations
University of Tennessee at Chattanooga
Chattanooga, United States
1 h-index
1 publications
32 citations
Fudan University
Shanghai, China
1 h-index
2 publications
52 citations
Institut des Géosciences de l'Environnement
Saint-Martin-d'Hères, France
1 h-index
2 publications
42 citations

Centre National de la Recherche Scientifique
Paris, France
1 h-index
2 publications
42 citations
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3 publications · 54 citations
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1 publications · 13 citations
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1 publications · 26 citations
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1 publications · 67 citations
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JAMC · 1 publications · 21 citations
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