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Found 4 papers, 3 papers with code
Perturbation-based Analysis of Compositional Data
Existing statistical methods for compositional data analysis are inadequate for many modern applications for two reasons.
Model-based causal feature selection for general response types
Discovering causal relationships from observational data is a fundamental yet challenging task.
The Projected Covariance Measure for assumption-lean variable significance testing
In this work we study the problem of testing the model-free null of conditional mean independence, i. e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$.
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables.
Statistics Theory Statistics Theory
