85 citations · 148 across the 7 of their papers we have counts for
6 papers · 1 filter
The Bayes Lepski's Method and Credible Bands through Volume of Tubular Neighborhoods
William Weimin Yoo, Aad W. van der Vaart
For a general class of priors based on random series basis expansion, we develop the Bayes Lepski's method to estimate unknown regression function. In this approach, the series tru…
Contributed Discussion to Uncertainty Quantification for the Horseshoe by Stéphanie van der Pas, Botond Szabó and Aad van der Vaart
William Weimin Yoo
We begin by introducing the main ideas of the paper under discussion. We discuss some interesting issues regarding adaptive component-wise credible intervals. We then briefly touch…
Adaptive Supremum Norm Posterior Contraction: Wavelet Spike-and-Slab and Anisotropic Besov Spaces
William Weimin Yoo, Vincent Rivoirard, Judith Rousseau
Supremum norm loss is intuitively more meaningful to quantify function estimation error in statistics. In the context of multivariate nonparametric regression with unknown error, w…
Contributed Discussion to Bayesian Solution Uncertainty Quantification for Differential Equations
William Weimin Yoo
We begin by introducing the main ideas of the paper under discussion, and we give a brief description of the method proposed. Next, we discuss an alternative approach based on B-sp…
Bayesian mode and maximum estimation and accelerated rates of contraction
William Weimin Yoo, Subhashis Ghosal
We study the problem of estimating the mode and maximum of an unknown regression function in the presence of noise. We adopt the Bayesian approach by using tensor-product B-splines…
Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression
William Weimin Yoo, Subhashis Ghosal
In the setting of nonparametric multivariate regression with unknown error variance, we study asymptotic properties of a Bayesian method for estimating a regression function f and…