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.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
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A hierarchical INLA approach decomposes non-linear biomarker scaling in joint longitudinal-survival models into a parametric baseline and data-driven smooth deviation via second-order random walk basis, enabling fast inference and linearity checks.
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.
citing papers explorer
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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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Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
A hierarchical INLA approach decomposes non-linear biomarker scaling in joint longitudinal-survival models into a parametric baseline and data-driven smooth deviation via second-order random walk basis, enabling fast inference and linearity checks.
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REX-SUB: A Scalable Subsampling Strategy for Modeling Large Spatial Datasets
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.