The Paired Swap Permutation Test is an exact non-parametric procedure that compares explanatory power of two dependent predictors via symmetric within-subject swapping for categorical data and ECDF mapping for continuous data.
Kari Lock Morgan and Donald B
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LGR samples balanced treatment assignments in high-dimensional experiments via continuous relaxation and SGLD, retaining valid inference through randomization tests while being orders of magnitude faster than prior methods.
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
Network knockoffs simulate synthetic features on the topological network to control FDR in dyadic regression, outperforming data splitting and standard knockoffs in simulations and a stream barrier application.
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
citing papers explorer
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Exact Comparison of Explanatory Strength of Two Dependent Predictors
The Paired Swap Permutation Test is an exact non-parametric procedure that compares explanatory power of two dependent predictors via symmetric within-subject swapping for categorical data and ECDF mapping for continuous data.
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Langevin-Gradient Rerandomization
LGR samples balanced treatment assignments in high-dimensional experiments via continuous relaxation and SGLD, retaining valid inference through randomization tests while being orders of magnitude faster than prior methods.
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Tests for Independence of High-Dimensional Nonstationary Time Series
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
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Network knockoffs: controlling false discovery in dyadic space
Network knockoffs simulate synthetic features on the topological network to control FDR in dyadic regression, outperforming data splitting and standard knockoffs in simulations and a stream barrier application.
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Restricted Multivariate Spatial Modeling
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.