A review on quantile regression for stochastic computer experiments
L Torossian, V Picheny, R Faivre, A Garivier - Reliability Engineering & …, 2020 - Elsevier
We report on an empirical study of the main strategies for quantile regression in the context
of stochastic computer experiments. To ensure adequate diversity, six metamodels are …
of stochastic computer experiments. To ensure adequate diversity, six metamodels are …
Consistency and robustness of kernel-based regression in convex risk minimization
A Christmann, I Steinwart - 2007 - projecteuclid.org
We investigate statistical properties for a broad class of modern kernel-based regression
(KBR) methods. These kernel methods were developed during the last decade and are …
(KBR) methods. These kernel methods were developed during the last decade and are …
Information science and statistics
Untitled Page 1 Page 2 Information Science and Statistics Series Editors: M. Jordan J.
Kleinberg B. Schölkopf Page 3 Information Science and Statistics For other titles published in …
Kleinberg B. Schölkopf Page 3 Information Science and Statistics For other titles published in …
Support vector machine with truncated pinball loss and its application in pattern recognition
L Yang, H Dong - Chemometrics and Intelligent Laboratory Systems, 2018 - Elsevier
Support vector machine (SVM) with pinball loss (PINSVM) has been recently proposed and
shown its advantages in pattern recognition. In this paper, we present a robust bounded loss …
shown its advantages in pattern recognition. In this paper, we present a robust bounded loss …
Data-driven optimization models for inventory and financing decisions in online retailing platforms
With data-driven optimization, this study investigates the sellers' inventory replenishment
and financial decisions, and lenders' interest rate decisions in online retailing platforms …
and financial decisions, and lenders' interest rate decisions in online retailing platforms …
On consistency and robustness properties of support vector machines for heavy-tailed distributions
Abstract Support Vector Machines (SVMs) are known to be consistent and robust for
classification and regression if they are based on a Lipschitz continuous loss function and …
classification and regression if they are based on a Lipschitz continuous loss function and …
On the feasibility of distributed kernel regression for big data
In Big Data applications, massive datasets with huge numbers of observations are frequently
encountered. To deal with such massive datasets, a divide-and-conquer scheme (eg …
encountered. To deal with such massive datasets, a divide-and-conquer scheme (eg …
[HTML][HTML] Support vector machine quantile regression approach for functional data: Simulation and application studies
C Crambes, A Gannoun, Y Henchiri - Journal of Multivariate Analysis, 2013 - Elsevier
The topic of this paper is related to quantile regression when the covariate is a function. The
estimator we are interested in, based on the Support Vector Machine method, was …
estimator we are interested in, based on the Support Vector Machine method, was …
Support vector machines: A robust prediction method with applications in bioinformatics
A Van Messem - Handbook of Statistics, 2020 - Elsevier
Over the last decades, classification and regression problems have been studied
extensively. This research led to the development of a vast number of methods for solving …
extensively. This research led to the development of a vast number of methods for solving …
Separability of reproducing kernel spaces
H Owhadi, C Scovel - Proceedings of the American Mathematical Society, 2017 - ams.org
Separability of reproducing kernel spaces Page 1 PROCEEDINGS OF THE AMERICAN
MATHEMATICAL SOCIETY Volume 145, Number 5, May 2017, Pages 2131–2138 http://dx.doi.org/10.1090/proc/13354 …
MATHEMATICAL SOCIETY Volume 145, Number 5, May 2017, Pages 2131–2138 http://dx.doi.org/10.1090/proc/13354 …