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Rethinking the Kolmogorov-Smirnov test of Goodness of fit in a compositional way

Author
Monti, G. S.; Mateu, G.; Ortego, M.I.; Pawlowsky, V.; Egozcue, J. J.
Type of activity
Presentation of work at congresses
Name of edition
49th Scientific meeting of the Italian Statistical Society
Date of publication
2018
Presentation's date
2018-06-22
Book of congress proceedings
49th Scientific meeting of the Italian Statistical Society (SIS 2018): Palermo, Italy: 20-22 june, 2018: book of short papers
First page
1
Last page
6
Project funding
Anàlisis de Dades Composicionals i Espacials. COmpositional and Spatial Data Analysis [COSDA]
Transferring compositional data methods into applied science and technology
Repository
http://hdl.handle.net/2117/169647 Open in new window
http://meetings3.sis-statistica.org/index.php/sis2018/49th/search/authors/view?firstName=Gianna&middleName=&lastName=Monti&affiliation=University%20of%20Milano%20Bicocca&country= Open in new window
Abstract
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...
Keywords
Aitchison distance, Generalized Extreme Value Distribution, Monte Carlo Simulations
Group of research
COSDA-UPC - COmpositional and Spatial Data Analysis

Participants