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Evaluating the complexity of some families of functional data
Bongiorno, Enea G. (Università del Piemonte Orientale)
Goia, Aldo (Università del Piemonte Orientale)
Vieu, Philippe (Université Paul Sabatier. Institut de Mathématiques de Toulouse)

Fecha: 2018
Resumen: In this paper we study the complexity of a functional data set drawn from particular processes by means of a two-step approach. The first step considers a new graphical tool for assessing to which family the data belong: the main aim is to detect whether a sample comes from a monomial or an exponential family. This first tool is based on a nonparametric kNN estimation of small ball probability. Once the family is specified, the second step consists in evaluating the extent of complexity by estimating some specific indexes related to the assigned family. It turns out that the developed methodology is fully free from assumptions on model, distribution as well as dominating measure. Computational issues are carried out by means of simulations and finally the method is applied to analyse some financial real curves dataset.
Derechos: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Lengua: Anglès.
Documento: article ; recerca ; publishedVersion
Materia: Small ball probability ; Log-volugram ; Random processes ; Complexity class ; Complexity index ; Knn estimation ; Functional data analysis
Publicado en: SORT : statistics and operations research transactions, Vol. 42 Núm. 1 (January-June 2018) , p. 27-44 (Articles) , ISSN 1696-2281

Adreça original: https://www.raco.cat/index.php/SORT/article/view/338207
DOI: 10.2436/20.8080.02.67

18 p, 517.3 KB

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