Research map / Probabilistic and scientific modeling / Molecules and proteins
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use plug-in estimator with ML conditional-mean estimates (Psi1) instead of naive E[YZ|X] decomposition | Psi1 expected conditional covariance · Simulation, low- and high-dimensional | empirical bias and CI coverage | Helped |
| Replace naive estimator with plug-in estimator that avoids excess bias, enabling valid Wald CIs | Psi1 expected conditional covariance · Low-dimensional simulation | 95% CI coverage | Helped |
| Scaled expected conditional covariance (Psi3) as the dependence measure for conditional independence testing | conditional independence test with X in R1 and R5 · Simulation vs KCI, CDI, CCIT | Type I error control and power | Helped |
| Scaled expected conditional covariance (Psi3) graph recovery instead of Gaussian graphical model | graph recovery with non-Gaussian, non-copula data · Case4, n=400, p=8 | ROC of graph recovery | Helped |
| Scaled expected conditional covariance (Psi3) graph recovery vs transelliptical graphical model in copula/transelliptical data | graph recovery · Case2 and Case3 (copula, transelliptical) | ROC of graph recovery | No clear effect |
| Use expected conditional correlation (Psi2) one-step estimator instead of Psi3 | Psi2 estimation · Simulation | practical performance | Hurt |
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