Results 31 to 40 of about 41,711 (262)

Signal Estimation Using Wavelet Analysis of Solution Monitoring Data for Nuclear Safeguards

open access: yesAxioms, 2013
Wavelets are explored as a data smoothing (or de-noising) option for solution monitoring data in nuclear safeguards. In wavelet-smoothed data, the Gibbs phenomenon can obscure important data features that may be of interest.
Tom Burr, Claire Longo
doaj   +1 more source

A Note on Nonparametric Estimation of Conditional Hazard Quantile Function [PDF]

open access: yesJournal of Risk Analysis and Crisis Response (JRACR), 2017
In this paper, we study an kernel estimator of the conditional hazard quantile function (CHQF) of a scalar response variable Y given a random variable (rv) X taking values in a semi-metric space and using the proposed estimator based of the kernel ...
El Hadj Hamel, Nadia Kadiri, Abbes Rabhi
doaj   +1 more source

Nonparametric Kernel Smoothing Methods. The sm library in Xlisp-Stat

open access: yesJournal of Statistical Software, 2001
In this paper we describe the Xlisp-Stat version of the sm library, a software for applying nonparametric kernel smoothing methods. The original version of the sm library was written by Bowman and Azzalini in S-Plus, and it is documented in their book ...
Luca Scrucca
doaj   +3 more sources

UJI KOEFISIEN VARIANSI KONSTAN DALAM REGRESI NONPARAMETRIK

open access: yesInfinity, 2015
ABSTRAK Tulisan ini membahas uji baru untuk hipotesis koefisien variansi konstan dalam model umum regresi nonparametrik. Uji ini didasarkan pada estimasi jarak antara kuadrat dari fungsi regresi dan fungsi varians.
Asri Ode Samura
doaj   +1 more source

Heat Kernels, Smoothness Estimates, and Exponential Decay [PDF]

open access: yesJournal of Fourier Analysis and Applications, 2012
25 ...
Boggess, Albert, Raich, Andrew
openaire   +2 more sources

Optimum kernels

open access: yesActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2004
Kernel smoothers belong to the most popular nonparametric functional estimates. They provide a simple way of finding structure in data. Kernel smoothing can be very well applied on the regression model.
Jitka Poměnková
doaj   +1 more source

Sex‐Stratified Association of Regional Dopamine Transporter Binding With Disease Progression in Amyotrophic Lateral Sclerosis

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To clarify the clinical relevance of dopamine transporter single‐photon emission computed tomography (DAT‐SPECT) abnormalities in amyotrophic lateral sclerosis (ALS), with a prespecified focus on sex‐stratified associations with disease progression and short‐term prognosis.
Tomoya Kawazoe   +7 more
wiley   +1 more source

Smoothing Module for Optimization Cranium Segmentation Using 3D Slicer

open access: yesInternational Journal of Applied Sciences and Smart Technologies, 2023
Anatomy is the most essential course in health and medical education to study parts of human body and also the function of it.  Cadaver is a media used by medical student to study anatomical subject.
Gilang Argya Dyaksa   +4 more
doaj   +1 more source

Stage‐Dependent β‐Synuclein Links MRI and Cognitive Decline in Alzheimer's Disease

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Synaptic degeneration drives cognitive decline in Alzheimer's disease (AD), but synaptic biomarkers are scarce. Brain‐enriched β‐synuclein emerged as a synaptic damage marker. We investigated its diagnostic, prognostic, and structural correlates across the AD continuum.
Ulaş Ay   +15 more
wiley   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli   +2 more
wiley   +1 more source

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