Treffer: Nonparametric Decomposition of Time Series Data with Inputs
Title:
Nonparametric Decomposition of Time Series Data with Inputs
Authors:
Source:
Communications in statistics. Simulation and computation. 41(8-10):1693-1710
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 3/4 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, Analyse mathématique, Mathematical analysis, Approximations et développements, Approximations and expansions, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, 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, Approximation numérique, Numerical approximation, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme, Algorithm, Algoritmo, Approximation numérique, Numerical approximation, Aproximación numérica, Approximation spline, Spline approximation, Aproximación esplín, Autocorrélation, Autocorrelation, Autocorrelación, Décomposition, Decomposition, Descomposición, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Estimation statistique, Statistical estimation, Estimación estadística, Lissage, Smoothing, Alisamiento, Modèle additif, Additive model, Modelo aditivo, Modèle linéaire, Linear model, Modelo lineal, Méthode décomposition, Decomposition method, Método descomposición, Méthode lissage, Smoothing methods, Méthode moindre carré, Least squares method, Método cuadrado menor, Méthode statistique, Statistical method, Método estadístico, Processus stochastique, Stochastic process, Proceso estocástico, Simulation numérique, Numerical simulation, Simulación numérica, Série temporelle, Time series, Serie temporal, 41A15, 62G08, 62M10, 65D07, Régression non paramétrique, Nonparametric regression, Variable dépendante, Dependent variable, 62J99, 65C60, 68U20, Additive models, Backfitting, Multicollinearity, Ordinary least squares
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
School of Statistics, University of the Philippines, Quezon city, Philippines
ISSN:
0361-0918
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.26164017
Database:
PASCAL Archive
Weitere Informationen
The backfilling algorithm commonly used in estimating additive models is used to decompose the component shares explained by a set of predictors on a dependent variable in the presence of linear dependencies (multicollinearity) among the predictors. Simulated and actual data show that the backfilling methods are superior in terms of predictive ability as the degree of multicollinearity worsens. Furthermore, the additive smoothing splines are especially superior when the linear model yield inadequate fit to the data and the prediclors exhibit extreme multicollinearity.