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Author: Joe, Harry
Resulting in 2 citations.
1. Cooke, Roger M.
Joe, Harry
Chang, Bo
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.
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.
2. Cooke, Roger M.
Joe, Harry
Chang, Bo
Vine Regression with Bayes Nets: A Critical Comparison with Traditional Approaches Based on a Case Study on the Effects of Breastfeeding on IQ
Risk Analysis published online (13 February 2021): DOI: 10.1111/risa.13695.
Also: https://doi.org/10.1111/risa.13695
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Wiley Online
Keyword(s): Bayesian; Breastfeeding; I.Q.; Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

Regular vines (R‐vines) copulas build high dimensional joint densities from arbitrary one‐dimensional margins and (conditional) bivariate copula densities. Vine densities enable the computation of all conditional distributions, though the calculations can be numerically intensive. Saturated continuous nonparametric Bayes nets (CNPBN) are regular vines. Computing regression functions from the vine copula density is termed vine regression. The epicycles of regression--including/excluding covariates, interactions, higher order terms, multicollinearity, model fit, transformations, heteroscedasticity, bias--are dispelled. One simply computes the regressions from the vine copula density. Only the question of finding an adequate vine copula remains. Vine regression is applied to a data set from the National Longitudinal Study of Youth relating breastfeeding to IQ. The expected effects of breastfeeding on IQ depend on IQ, on the baseline level of breastfeeding, on the duration of additional breastfeeding and on the values of other covariates. A child given two weeks breastfeeding can expect to increase his/her IQ by 1.5-2 IQ points by adding 10 weeks of breastfeeding, depending on values of other covariates. A child given two years breastfeeding can expect to gain from 0.48-0.65 IQ points from 10 additional weeks. Adding 10 weeks breastfeeding to each of the 3,179 children in this data set has a net present value $50,700,000 according to the Bayes net, compared to $29,000,000 according to the linear regression.
Bibliography Citation
Cooke, Roger M., Harry Joe and Bo Chang. "Vine Regression with Bayes Nets: A Critical Comparison with Traditional Approaches Based on a Case Study on the Effects of Breastfeeding on IQ." Risk Analysis published online (13 February 2021): DOI: 10.1111/risa.13695.