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Título:
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Rethinking the Kolmogorov-Smirnov test of Goodness of fit in a compositional way
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Autor/a:
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Monti, Gianna S.; Mateu Figueras, Gloria; Ortego Martínez, María Isabel; Pawlowsky Glahn, Vera; Egozcue Rubí, Juan José
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Otros autores:
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Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental; Universitat Politècnica de Catalunya. COSDA-UPC - COmpositional and Spatial Data Analysis |
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Abstract:
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The Kolmogorov Smirnov test (KS) is a well known test used to asses how a set of observations is significantly different from the probability model specified under the null hypothesis. The KS test statistic quantifies the distance between the empirical distribution function and the hypothetical one. The modification introduced in Monti et al. (2017) consists of computing the mentioned distances as Aitchison distances. In this contribution, we suggest a further modification of the latter test and investigate, by simulation, the asymptotic distribution of the proposed test statistic, checking the appropriateness of a Generalized Extreme Value (GEV) Distribution. The properties of the asymptotic distribution are studied via Monte Carlo simulations. |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Modelització matemàtica
-Numerical analysis--Simulation methods
-Generalized Extreme Value Distribution
-Aitchison distance
-Monte
Carlo Simulations
-Anàlisi numèrica
-Classificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations |
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Derechos:
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Tipo de documento:
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Artículo - Versión presentada
Objeto de conferencia |
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Editor:
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Pearson
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Compartir:
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