Nonparametric validation of similar distributions and assessment of goodness of fit

Axel Munk; Claudia Czado

doi:10.1111/1467-9868.00121

Nonparametric validation of similar distributions and assessment of goodness of fit

Axel Munk, Claudia Czado

Research output: Contribution to journal › Article › peer-review

68 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	223-241
Number of pages	19
Journal	Journal of the Royal Statistical Society. Series B: Statistical Methodology
Volume	60
Issue number	1
DOIs	https://doi.org/10.1111/1467-9868.00121
State	Published - 1998
Externally published	Yes

Keywords

Clinically relevant difference
Equivalence testing
Mallows distance
Model validation
Population equivalence
Testing goodness of fit
p-value curve

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10.1111/1467-9868.00121

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Nonparametric validation of similar distributions and assessment of goodness of fit

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Keywords

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