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.
The Annals of Mathematical Statistics , pages=
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The paper proves sharp O(ε² log(1/ε)/log log(1/ε)) regret bounds for unregularized Bayes rules with compactly supported priors via polynomial approximation, improving on prior regularized results with extra log factors.
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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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Sharp regret-Hellinger bounds for Gaussian empirical Bayes via polynomial approximation
The paper proves sharp O(ε² log(1/ε)/log log(1/ε)) regret bounds for unregularized Bayes rules with compactly supported priors via polynomial approximation, improving on prior regularized results with extra log factors.