• Confidence intervals of the ha...

Treffer: Confidence intervals of the hazard rate function for discrete distributions using mixtures

Title:
Confidence intervals of the hazard rate function for discrete distributions using mixtures
Authors:
KARLIS, Dimitris, PATILEA, Valentin
Source:
Advances in mixture modelsComputational statistics & data analysis. 51(11):5388-5401
Publisher Information:
Amsterdam: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 1/4 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, Analyse multivariable, Multivariate analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse donnée, Data analysis, Análisis datos, Bootstrap, Calcul statistique, Statistical computation, Cálculo estadístico, Durée vie, Lifetime, Tiempo vida, Echelle temps, Time scale, Escala tiempo, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Fonction discrète, Discrete function, Función discreta, Fonction hasard, Hazard function, Función azar, Fonction répartition, Distribution function, Función distribución, Fonction taux, Rate function, Función tasa, Intervalle confiance, Confidence interval, Intervalo confianza, Loi discrète, Discrete distribution, Ley discreta, Loi série entière, Power series distribution, Ley serie potencia, Maximum vraisemblance, Maximum likelihood, Maxima verosimilitud, Modèle statistique, Statistical model, Modelo estadístico, Mélange loi probabilité, Mixed distribution, Mezcla ley probabilidad, Mélangeage, Mixing, Mezclado, Méthode rééchantillonnage, Resampling method, Méthode statistique, Statistical method, Método estadístico, Série entière, Power series, Serie potencias, 37A25, 60E05, 62E17, 62F25, 62F40, 62G09, 62G15, Modèle discret, Asymptotic normality, Bootstrap confidence intervals, Nonparametric maximum likelihood, Power series distributions
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, Athens University of Economics, 76 Patission Str, Athens 10434, Greece
CREST-ENSAI, Campus de Ker Lann, Rue Blaise Pascal BP 37203, 35172 Bruz, France
ISSN:
0167-9473
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18830900
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.18830900
Database:
PASCAL Archive

Weitere Informationen

The statistical models and methods for lifetime data mainly deal with continuous nonnegative lifetime distributions. However, discrete lifetimes arise in various common situations where either the clock time is not the best scale for measuring lifetime or the lifetime is measured discretely. In most settings involving lifetime data, the population under study is not homogenous. Mixture models, in particular mixtures of discrete distributions, provide a natural answer to this problem. Nonparametric mixtures of power series distributions are considered, as for instance nonparametric mixtures of Poisson laws or nonparametric mixtures of geometric laws. The mixing distribution is estimated by nonparametric maximum likelihood (NPML). Next, the NPML estimator is used to build estimates and confidence intervals for the hazard rate function of the discrete lifetime distribution. To improve the performance of the confidence intervals, a bootstrap procedure is considered where the estimated mixture is used for resampling. Various bootstrap confidence intervals are investigated and compared to the confidence intervals obtained directly from the NPML estimates.