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Nonparametric validation of similar distributions and assessment of goodness of fit
Author(s) -
Munk Axel,
Czado Claudia
Publication year - 1998
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00121
Subject(s) - goodness of fit , nonparametric statistics , mathematics , equivalence (formal languages) , statistics , similarity (geometry) , population , type i and type ii errors , bioequivalence , econometrics , computer science , discrete mathematics , artificial intelligence , medicine , environmental health , pharmacology , image (mathematics) , bioavailability
In this paper the problem of assessing the similarity of two cumulative distribution functions F and G is considered. An asymptotic test based on an α‐trimmed version of Mallows distance Γ α ( F , G ) between F and G is suggested, thus demonstrating the similarity of F and G within a preassigned Γ α ( F , G ) neighbourhood at a controlled type I error rate. The test proposed is applied to the validation of goodness of fit and for the nonparametric assessment of bioequivalence. It is shown that Γ α ( F , G ) can be interpreted as average and population equivalence. Our approach is illustrated by various examples.