Analysis of Car Type Preferences Among Students Based on Seemingly Unrelated Regression
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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15 صفحه اولHighly accurate likelihood analysis for the seemingly unrelated regression problem
The linear and nonlinear seemingly unrelated regression problem with general error distribution is analyzed using recent likelihood theory that arguably provides the definitive distribution for assessing a scalar parameter; this involves implicit but well defined conditioning and marginalization for determining intrinsic measures of departure. Highly accurate p-values are obtained for the key d...
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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...
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ژورنال
عنوان ژورنال: Transportation Research Record: Journal of the Transportation Research Board
سال: 2017
ISSN: 0361-1981,2169-4052
DOI: 10.3141/2666-10