Results 1 to 10 of about 41,219 (164)

Anatomically constrained volumetric smoothing enhances fMRI reliability while avoiding smoothing artifacts [PDF]

Frontiers in Neuroimaging
IntroductionSmoothing fMRI data prior to analysis is a fundamental and widely used technique to increase sensitivity. Unconstrained smoothing can also reduce the spatial specificity of the analysis by introducing artifacts in the data.
David G. Ellis, Michele R. Aizenberg
doaj   +2 more sources

Kernel Smoothing in Partial Linear Models

Journal of the Royal Statistical Society Series B: Statistical Methodology, 1988
SUMMARY Kernel smoothing is studied in partial linear models, i.e. semiparametric models of the form yi=ξi′β+f(ti)+εi(1⩽i⩽n), where the ξi are fixed known p vectors, β is an unknown vector parameter and f is a smooth but unknown function.
exaly   +3 more sources

Spline Smoothing: The Equivalent Variable Kernel Method

Annals of Statistics, 1984
The cubic spline estimator of the regression curve is related with the existence of a weight function which enables to perform a nonparametric estimation of the function. The relation between this method and the kernel approach is fixed under suitable conditions with the aims of giving an intuitive insight into spline-smoothing methods. The main result
Bernard W Silverman
exaly   +4 more sources

Fast Kernel Smoothing in R with Applications to Projection Pursuit

Journal of Statistical Software, 2022
This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main kernel computations are implemented in C++, and are wrapped in simple, intuitive and versatile R functions.
David P. Hofmeyr
doaj   +1 more source

Connectome spatial smoothing (CSS): Concepts, methods, and evaluation

NeuroImage, 2022
Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds—if not thousands—of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography ...
Sina Mansour L, Caio Seguin, Robert E. Smith, Andrew Zalesky   +3 more
doaj   +1 more source

Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization [PDF]

المجلة العراقية للعلوم الاحصائية, 2020
In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this
Zinah Basheer, Zakariya Algamal
doaj   +1 more source

Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models

Revstat Statistical Journal, 2021
This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing
Ersin Yilmaz , Bahadir Yuzbasi , Dursun Aydin   +2 more
doaj   +1 more source

Distributed Smoothed Tree Kernel [PDF]

Italian Journal of Computational Linguistics, 2014
In this paper we explore the possibility to merge the world of Compositional Distributional Semantic Models (CDSM) with Tree Kernels (TK). In particular, we will introduce a specific tree kernel (smoothed tree kernel, or STK) and then show that is possibile to approximate such kernel with the dot product of two vectors obtained compositionally from the
Ferrone, L, ZANZOTTO, FABIO MASSIMO
openaire   +5 more sources

Adversarially Robust Kernel Smoothing

, 2021
We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization. Our method is motivated by the convex analysis perspective of distributionally robust optimization based on probability metrics, such as the Wasserstein distance and the maximum mean discrepancy.
Zhu, Jia-Jie, Kouridi, Christina, Nemmour, Yassine, Schölkopf, Bernhard   +3 more
openaire   +5 more sources

A Fast Two-Stage Bilateral Filter Using Constant Time O(1) Histogram Generation

Sensors, 2022
Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often ...
Sheng-Wei Cheng, Yi-Ting Lin, Yan-Tsung Peng   +2 more
doaj   +1 more source