Results 101 to 110 of about 5,004,642 (164)

UNCERTAINTY BOUNDS FOR KERNEL-BASED REGRESSION: A BAYESIAN SPS APPROACH

, 2018
This paper shows that kernel-based estimates of unknown input-output maps can be complemented with uncertainty bounds more robust than those commonly derived in the Gaussian regression framework.
Care, Algo, Algo Care, Campi, Marco C., Gianluigi Pillonetto, Marco C. Campi, Pillonetto, Gianluigi   +5 more
core   +1 more source

Block-Adaptive Kernel-Based CDMA Multiuser Detector

, 2002
The paper investigates the application of a recently introduced learning technique, called the relevance vector machine (RVM) to construct a block-adaptive kernel-based nonlinear multiuser detector (MUD) for direct-sequence code-division multiple-access (
Hanzo, L., Chen, S.
core   +1 more source

Bayesian Kernel Machine Regression via Random Fourier Features for Estimating Joint Health Effects of Multiple Exposures

Environmental epidemiology has traditionally examined single exposure one at a time. Advances in exposure assessment and statistical methods now enable studies of multiple exposures and their combined health impacts. Bayesian Kernel Machine Regression (BKMR) is a widely used approach to flexibly estimates joint, nonlinear effects of multiple exposures.
Zhang, Danlu, Eick, Stephanie M., Chang, Howard H.   +2 more
openaire   +2 more sources

Consistency and robustness of kernel based regression [PDF]

We investigate properties of kernel based regression (KBR) methods which are inspired by the convex risk minimization method of support vector machines.
Christmann, Andreas, Steinwart, Ingo
core  

Using three statistical methods to analyze the associations between a mixture of multi-nutrients and risk of mild cognitive impairment in an elderly population in Northern China

Scientific Reports
Few studies have considered nutrients as a mixture and their impact on Mild Cognitive Impairment (MCI). The generalized linear regression (GLM), weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models are fitted to ...
Xian Gao, Yan Wang, Qingxia Li, Xin Huang, Yan Sun, Yutian Zhou, Huichen Zhu, Shiyao Liu, Yuxia Ma   +8 more
doaj   +1 more source

Non-parametric Bayesian kernel models

, 2007
Kernel models for classification and regression have emerged as widely applied tools in statistics and machine learning. We discuss a Bayesian framework and theory for kernel methods, providing a new rationalisation of kernel regression based on non ...
Feng Liang, Kai Mao, Ming Liao
core  

Bayesian Analysis for Penalized Spline Regression Using WinBUGS [PDF]

Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression ...
Matthew P. Wand, David Ruppert, Ciprian M. Crainiceanu   +2 more
core  

Association of serum essential and non-essential elements with dyslipidemia in patients with cardiovascular disease in Western Iran: an application of Bayesian kernel machine regression. [PDF]

BMC Cardiovasc Disord
Nakhaee S, Manoochehri Z, Mahjoubian M, Khodamoradi M, Mansouri B.   +4 more
europepmc   +1 more source

Sparse Bayesian Kernel Logistic Regression

, 2008
. In this paper we present a simple hierarchical Bayesian treatment of the sparse kernel logistic regression (KLR) model based MacKay’s evidence approximation.
Nicola L. C. Talbot, Gavin C. Cawley
core  

Software Application Profile: BKMR-CMA: a novel R command for Bayesian mediation analysis in environmental mixture studies. [PDF]

Int J Epidemiol
Chai Z, Domingo-Relloso A, Devick KL, Mazumndar M, Navas-Acien A, Coull BA, Valeri L.   +6 more
europepmc   +1 more source