Kernel density estimator and recursive kernel predictive processes converge weakly almost surely, with the classic version limiting to compact support and the recursive version to non-compact support.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
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PRx combines kernel weight localization with predictive recursion for fast semiparametric density regression, yielding consistent estimators for unmixed parameters and competitive performance at low computational cost.
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
citing papers explorer
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Predictive Inference via Kernel Density Estimates
Kernel density estimator and recursive kernel predictive processes converge weakly almost surely, with the classic version limiting to compact support and the recursive version to non-compact support.
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Fast Semiparametric Density Regression with Weight-localized Predictive Recursion
PRx combines kernel weight localization with predictive recursion for fast semiparametric density regression, yielding consistent estimators for unmixed parameters and competitive performance at low computational cost.
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Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.