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On an additive partial correlation operator and nonparametric estimation of graphical models
2016
Biometrika
We introduce an additive partial correlation operator as an extension of partial correlation to the nonlinear setting, and use it to develop a new estimator for nonparametric graphical models. Our graphical models are based on additive conditional independence, a statistical relation that captures the spirit of conditional independence without having to resort to high-dimensional kernels for its estimation. The additive partial correlation operator completely characterizes additive conditional
doi:10.1093/biomet/asw028
pmid:29422689
pmcid:PMC5793672
fatcat:4yllodfgzvanffj7dp5dmht4ny