A nonparametric Bayesian seemingly unrelated regression model
نویسندگان
چکیده
منابع مشابه
Bayesian nonparametric sparse seemingly unrelated regression model (SUR)∗
Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a high dimensional setting or when applied to large panel of time series, these models require a large number of parameters to be estimated and suffer of inferential problems. To avoid overparametrization and overfitting issues, we propose a hierarchical Dirichlet process prior for ...
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Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covaria...
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Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covaria...
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ژورنال
عنوان ژورنال: Korean Journal of Applied Statistics
سال: 2016
ISSN: 1225-066X
DOI: 10.5351/kjas.2016.29.4.627