Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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stat.ME 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
A model-free estimator for causal effects in two-sample Mendelian randomization that is consistent and asymptotically normal under population heterogeneity between samples.
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Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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Multi-Fidelity Quantile Regression
A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
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A Robust Framework for Two-Sample Mendelian Randomization under Population Heterogeneity
A model-free estimator for causal effects in two-sample Mendelian randomization that is consistent and asymptotically normal under population heterogeneity between samples.