SEMIPARAMETRIC ESTIMATION OF THE ERROR DISTRIBUTION IN MULTIVARIATE REGRESSION USING COPULAS

DOI10.1111/j.1467-842X.2007.00483.xzbMath1136.62032MaRDI QIDQ5449892

Gunky Kim, Mervyn J. Silvapullé, Paramsothy Silvapulle

Publication date: 19 March 2008

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

zbMATH Keywords

value-at-riskassociationestimating equationpseudolikelihooddependence parameter

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05)

Related Items

A copula approach for dependence modeling in multivariate nonparametric time series ⋮ Comparison of semiparametric maximum likelihood estimation and two-stage semiparametric estimation in copula models ⋮ Estimating the error distribution in multivariate heteroscedastic time-series models

Uses Software

• Matlab

Cites Work

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• Weighted empirical processes in dynamic nonlinear models.
• Biometrika Centenary: Survival analysis
• On assessing the association for bivariate current status data
• Statistical Inference Procedures for Bivariate Archimedean Copulas
• Goodness-of-fit Procedures for Copula Models Based on the Probability Integral Transformation
• Inferences on the Association Parameter in Copula Models for Bivariate Survival Data
• Modelling multivariate failure time associations in the presence of a competing risk
• Estimating the Association Parameter for Copula Models Under Dependent Censoring

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