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Source: AStA Advances in Statistical Analysis
Resulting in 1 citation.
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Cooke, Roger M. Joe, Harry Chang, Bo |
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Vine Copula Regression for Observational Studies AStA Advances in Statistical Analysis published online (5 June 2019): DOI: 10.1007/s10182-019-00353-5. Also: https://link.springer.com/article/10.1007/s10182-019-00353-5 Cohort(s): Children of the NLSY79, NLSY79 Publisher: Springer Keyword(s): Breastfeeding; I.Q.; Peabody Picture Vocabulary Test (PPVT); Statistical Analysis Permission to reprint the abstract has not been received from the publisher. If explanatory variables and a response variable of interest are simultaneously observed, then fitting a joint multivariate density to all variables would enable prediction via conditional distributions. Regular vines or vine copulas with arbitrary univariate margins provide a rich and flexible class of multivariate densities for Gaussian or non-Gaussian dependence structures. The density enables calculation of all regression functions for any subset of variables conditional on any disjoint set of variables, thereby avoiding issues of transformations, heteroscedasticity, interactions, and higher-order terms. Only the question of finding an adequate vine copula remains. Heteroscedastic prediction inferences based on vine copulas are illustrated with two data sets, including one from the National Longitudinal Study of Youth relating breastfeeding to IQ. Some usual methods based on linear and quadratic equations are shown to have some undesirable inferences. |
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Bibliography Citation
Cooke, Roger M., Harry Joe and Bo Chang. "Vine Copula Regression for Observational Studies." AStA Advances in Statistical Analysis published online (5 June 2019): DOI: 10.1007/s10182-019-00353-5.
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