Result: Estimation and Simulation for the M-Wright Function
Title:
Estimation and Simulation for the M-Wright Function
Authors:
Source:
Communications in statistics. Theory and methods. 41(7-9):1466-1477
Publisher Information:
Philadelphia, PA: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Généralités, General topics, Inférence paramétrique, Parametric inference, Inférence non paramétrique, Nonparametric inference, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme, Algorithm, Algoritmo, Analyse numérique, Numerical analysis, Análisis numérico, Bootstrap, Comportement asymptotique, Asymptotic behavior, Comportamiento asintótico, Distribution statistique, Statistical distribution, Distribución estadística, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Estimation paramètre, Parameter estimation, Estimación parámetro, Estimation statistique, Statistical estimation, Estimación estadística, Fonction Wright, Wright function, Función Wright, Génération nombre aléatoire, Random number generation, Generación número aleatorio, Mélange loi probabilité, Mixed distribution, Mezcla ley probabilidad, Méthode jackknife, Jackknife method, Método jackknife, Méthode moment, Moment method, Método momento, Méthode rééchantillonnage, Resampling method, Méthode statistique, Statistical method, Método estadístico, Méthode stochastique, Stochastic method, Método estocástico, Normalité asymptotique, Asymptotic normality, Normalidad asintótica, Simulation, Simulación, Variable aléatoire, Random variable, Variable aléatoria, 60E07, 62E20, 62F12, 62F40, 62G20, 65C10, Estimation paramétrique, Loi asymptotique, 60G22, 62E15, 62G05, 97K60, Estimation, M-Wright function, Method-of-moments, Primary 62E15, Secondary 97K80, Stable distribution
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics and Statistics, College of Engineering and Science, Louisiana Tech University, Ruston, Louisiana, United States
ISSN:
0361-0926
Rights:
Copyright 2015 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
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.25906557
Database:
PASCAL Archive
Further Information
In this article, a structural form of an M-Wright distributed random variable is derived. The mixture representation then led to a random number generation algorithm. A formal parameter estimation procedure is also proposed. This procedure is needed to make the M-Wright function usable in practice. The asymptotic normality of the estimator is established as well. The estimator and the random number generation algorithm are then tested using synthetic data.