Cross-fitted covariance calibration enables chi-squared hypothesis tests for penalized estimating equations that are robust to covariance misspecification under correct conditional mean.
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A Bayesian model-X knockoff procedure using parameter-expanded latent knockoffs from a Gaussian graphical model on covariates and modified spike-and-slab priors controls Bayesian FDR for variable selection in finite samples when the covariate distribution is known.
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Hypothesis Testing for Penalized Estimating Equations with Cross-Fitted Covariance Calibration
Cross-fitted covariance calibration enables chi-squared hypothesis tests for penalized estimating equations that are robust to covariance misspecification under correct conditional mean.
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Bayesian Controlled FDR Variable Selection via Parameter-Expanded Latent Knockoffs
A Bayesian model-X knockoff procedure using parameter-expanded latent knockoffs from a Gaussian graphical model on covariates and modified spike-and-slab priors controls Bayesian FDR for variable selection in finite samples when the covariate distribution is known.