Treffer: Wavelet goodness-of-fit test for dependent data

Title:
Wavelet goodness-of-fit test for dependent data
Authors:
BOCHKINA, N. A
Source:
5th St Petersburg workshop on simulation, St. Petersburg State University, St. Petersburg, Russia, 26 June-2 July 2005Journal of statistical planning and inference. 137(8):2593-2612
Publisher Information:
Amsterdam; Lausanne; New York,NY: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Analyse de fourier, Fourier analysis, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Généralités, General topics, Lois de probabilités, Distribution theory, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Analyse corrélation, Correlation analysis, Análisis correlación, Corrélation, Correlation, Correlación, Décision statistique, Statistical decision, Decisión estadística, Décomposition, Decomposition, Descomposición, Fonction Riemann, Riemann function, Función Riemann, Fonction corrélation, Correlation function, Función correlación, Fonction répartition, Distribution function, Función distribución, Fonction zêta, Zeta function, Función zeta, Loi marginale, Marginal distribution, Ley marginal, Matrice aléatoire, Random matrix, Matriz aleatoria, Méthode décomposition, Decomposition method, Método descomposición, Méthode statistique, Statistical method, Método estadístico, Ondelette, Wavelets, Orthogonalité, Orthogonality, Ortogonalidad, Statistique test, Test statistic, Estadística test, Test ajustement, Goodness of fit test, Prueba ajuste, Test hypothèse, Hypothesis test, Test hipótesis, Test statistique, Statistical test, Test estadístico, Transformation ondelette, Wavelet transformation, Transformación ondita, Valeur propre, Eigenvalue, Valor propio, Zéro de fonction, Zero of function, Cero de función, 11Mxx, 42C40, 60E05, 62E17, 62E20, 62F03, 62F05, 62G10, 62G30, 62H15, 62H20, 65T60, Fonction répartition empirique, Empirical distribution function, Loi asymptotique, Dependent data, Goodness-of-fit test, Riemann's zeta function
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Centre for Biostatistics, Imperial College, London W2 1PG, United Kingdom
ISSN:
0378-3758
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18790967
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.18790967
Database:
PASCAL Archive

Weitere Informationen

We introduce a new goodness-of-fit test which can be applied to hypothesis testing about the marginal distribution of dependent data. We derive a new test for the equivalent hypothesis in the space of wavelet coefficients. Such properties of the wavelet transform as orthogonality, localisation and sparsity make the hypothesis testing in wavelet domain easier than in the domain of distribution functions. We propose to test the null hypothesis separately at each wavelet decomposition level to overcome the problem of bi-dimensionality of wavelet indices and to be able to find the frequency where the empirical distribution function differs from the null in case the null hypothesis is rejected. We suggest a test statistic and state its asymptotic distribution under the null and under some of the alternative hypotheses. We also discuss how the wavelet test can be applied to a hypothesis about the correlation structure of a sequence if its marginal distribution is known. We apply the test to compare the correlation structure of the zeroes of the Riemann zeta function to the correlation structure of the eigenvalues of unitary random matrices.