• SiZer Inference for Varying Co...

Result: SiZer Inference for Varying Coefficient Models

Title:
SiZer Inference for Varying Coefficient Models
Authors:
ZHANG, Hui-Guo, MEI, Chang-Lin
Source:
Communications in statistics. Simulation and computation. 41(8-10):1944-1959
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1 p.3/4
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, Inférence paramétrique, Parametric inference, Analyse multivariable, Multivariate analysis, Inférence linéaire, régression, Linear inference, regression, 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, Coefficient régression, Regression coefficient, Coeficiente regresión, Coefficient variation, Variation coefficient, Coeficiente variación, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Estimation statistique, Statistical estimation, Estimación estadística, Intervalle confiance, Confidence interval, Intervalo confianza, Modèle statistique, Statistical model, Modelo estadístico, Méthode statistique, Statistical method, Método estadístico, Régression statistique, Statistical regression, Regresión estadística, Simulation numérique, Numerical simulation, Simulación numérica, Simulation statistique, Statistical simulation, Simulación estadística, 62F25, 62G15, 62Jxx, Estimation paramétrique, 62G08, Scale-space, SiZer, Significant features, Simultaneous confidence intervals, Varying coefficient model
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
College of Mathematics and System Science, Xinjiang University, Urumqi, China
ISSN:
0361-0918
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26164036
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
Notes:
Mathematics
Accession Number:
edscal.26164036
Database:
PASCAL Archive

Further Information

Varying coefficient models are a useful statistical tool to explore dynamic patterns of a regression relationship, in which the variation features of the regression coefficients are taken as the main evidence to reflect the dynamic relationship between the response and the explanatory variables. In this study, we propose a SiZer approach as a visually diagnostic device to uncover the statistically significant features of the coefficients. This method can highlight the significant structures of the coefficients under different scales and can therefore extract relatively full information in the data. The simulation studies and real-world data analysis show that the SiZer approach performs satisfactorily in mining the significant features of the coefficients.