Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Yoo, William Weimin"'
Autor:
Babic, Anatoliy, Bansal, Harshit, Finocchio, Gianluca, Golak, Julian, Peletier, Mark, Portegies, Jim, Stegehuis, Clara, Tyagi, Anuj, Vincze, Roland, Yoo, William Weimin
SciSports is a Dutch startup company specializing in football analytics. This paper describes a joint research effort with SciSports, during the Study Group Mathematics with Industry 2018 at Eindhoven, the Netherlands. The main challenge that we addr
Externí odkaz:
http://arxiv.org/abs/1808.04550
Autor:
Yoo, William Weimin
I begin by summarizing key ideas of the paper under discussion. Then I will talk about a graphical modeling perspective, posterior contraction rates and alternative methods of aggregation. Moreover, I will also discuss possible applications of the st
Externí odkaz:
http://arxiv.org/abs/1806.11427
Autor:
Yoo, William Weimin
Publikováno v:
Bayesian Anal., Volume 13, Number 2 (2018), 599-600
I begin my discussion by giving an overview of the main results. Then I proceed to touch upon issues about whether the credible ball constructed can be interpreted as a confidence ball, suggestions on reducing computational costs, and posterior consi
Externí odkaz:
http://arxiv.org/abs/1803.05066
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 truncation point is determined based on a stopping rule that balances the
Externí odkaz:
http://arxiv.org/abs/1711.06926
Autor:
Yoo, William Weimin
Publikováno v:
Bayesian Anal. Volume 13, Number 1 (2018), 284-285
I begin my discussion by summarizing the methodology proposed and new distributional results on multivariate log-Gamma derived in the paper. Then, I draw an interesting connection between their work with mean field variational Bayes. Lastly, I make s
Externí odkaz:
http://arxiv.org/abs/1711.06477
Autor:
Yoo, William Weimin
Publikováno v:
Bayesian Anal. Volume 12, Number 4 (2017), 1262-1263
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 upon the concepts of self-similarity and excessive bias restriction.
Externí odkaz:
http://arxiv.org/abs/1710.05987
Supremum norm loss is intuitively more meaningful to quantify function estimation error in statistics. In the context of multivariate nonparametric regression with unknown error, we propose a Bayesian procedure based on spike-and-slab prior and wavel
Externí odkaz:
http://arxiv.org/abs/1708.01909
Autor:
Yoo, William Weimin
Publikováno v:
Bayesian Anal. 11, no 4 (2016), 1285-1293
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-spline expansion, and lastly we make some comments on the method's conve
Externí odkaz:
http://arxiv.org/abs/1611.05843
Autor:
Yoo, William Weimin, Ghosal, Subhashis
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 and endowing the coefficients with Gaussian priors. In the usual fixe
Externí odkaz:
http://arxiv.org/abs/1608.03913
Autor:
Yoo, William Weimin, Ghosal, Subhashis
Publikováno v:
Ann. Statist. Volume 44, Number 3 (2016), 1069-1102
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 its mixed partial derivatives. We use a random series of tensor produc
Externí odkaz:
http://arxiv.org/abs/1411.6716