• Modelling nonlinear count time...

Treffer: Modelling nonlinear count time series with local mixtures of poisson autoregressions

Title:
Modelling nonlinear count time series with local mixtures of poisson autoregressions
Authors:
CARVALHO, Alexandre X, TANNER, Martin A
Source:
Advances in mixture modelsComputational statistics & data analysis. 51(11):5266-5294
Publisher Information:
Amsterdam: Elsevier Science, 2007.
Publication Year:
2007
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, Analyse multivariable, Multivariate analysis, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series 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, Autorégression, Autoregression, Autoregresión, Calcul statistique, Statistical computation, Cálculo estadístico, Covariable, Covariate, Estimation moyenne, Mean estimation, Estimación promedio, Fonction répartition, Distribution function, Función distribución, Loi conditionnelle, Conditional distribution, Ley condicional, Loi multinomiale, Multinomial distribution, Ley multinomial, Maximum vraisemblance, Maximum likelihood, Maxima verosimilitud, Modèle non linéaire, Non linear model, Modelo no lineal, Modèle régression, Regression model, Modelo regresión, Mélange loi probabilité, Mixed distribution, Mezcla ley probabilidad, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Normalité asymptotique, Asymptotic normality, Normalidad asintótica, Processus autorégressif, Autoregressive processes, Régression statistique, Statistical regression, Regresión estadística, Simulation, Simulación, Sélection modèle, Model selection, Selección modelo, Série temporelle, Time series, Serie temporal, Temps local, Local time, Tiempo local, 06Axx, 60E05, 60J55, 62E17, 62E20, 62F07, 62Jxx, 62M10, 65C05, Méthode sélection, Selection method, Variable dépendante, Dependent variable, Count data, Maximum likelihood estimation, Mixtures-of-experts, Nonlinear time series, Poisson regression
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
institute of Applied Economics Research, Brazil
Northwestern University, United States
ISSN:
0167-9473
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18830891
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.18830891
Database:
PASCAL Archive

Weitere Informationen

A novel class of nonlinear models is studied based on local mixtures of autoregressive Poisson time series. The proposed model has the following construction: at any given time period, there exist a certain number of Poisson regression models, denoted as experts, where the vector of covariates may include lags of the dependent variable. Additionally, the existence of a latent multinomial variable is assumed, whose distribution depends on the same covariates as the experts. The latent variable determines which Poisson regression is observed. This structure is a special case of the mixtures-of-experts class of models, which is considerably flexible in modelling the conditional mean function. A formal treatment of conditions to guarantee the asymptotic normality of the maximum likelihood estimator is presented, under stationarity and nonstationarity. The performance of common model selection criteria in selecting the number of experts is explored via Monte Carlo simulations. Finally, an application to a real data set is presented, in order to illustrate the ability of the proposed structure to flexibly model the conditional distribution function.