Seemingly Unrelated Regression Equations for Developing a Pavement Performance Model
نویسندگان
چکیده
منابع مشابه
Bayesian Geoadditive Seemingly Unrelated Regression
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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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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We propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model — with a conditional quantile restriction for each equation — in which the conditional distributions of errors given regressors are unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotical...
متن کاملBayesian Geoadditive Seemingly Unrelated Regression 1
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...
متن کاملUsing Bootstrapped Confidence Intervals for Improved Inferences with Seemingly Unrelated Regression Equations
The usual standard errors for the regression coe cients in a Seemingly Unrelated Regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t-ratios rather than to standard errors. 3
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ژورنال
عنوان ژورنال: Modern Applied Science
سال: 2015
ISSN: 1913-1852,1913-1844
DOI: 10.5539/mas.v9n13p199