Results 41 to 50 of about 120,823 (294)

Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution

Information, 2023
Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version.
Asif Hussain Khan, Christian Micheloni, Niki Martinel   +2 more
doaj   +1 more source

Optimal Estimation for the Functional Cox Model

, 2016
Functional covariates are common in many medical, biodemographic, and neuroimaging studies. The aim of this paper is to study functional Cox models with right-censored data in the presence of both functional and scalar covariates. We study the asymptotic
Qu, Simeng, Wang, Jane-Ling, Wang, Xiao
core   +1 more source

Fluid Biomarkers of Disease Burden and Cognitive Dysfunction in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Identifying objective biomarkers for progressive supranuclear palsy (PSP) is crucial to improving diagnosis and establishing clinical trial and treatment endpoints. This study evaluated fluid biomarkers in PSP versus controls and their associations with regional 18F‐PI‐2620 tau‐PET, clinical, and cognitive outcomes.
Roxane Dilcher, Charles B. Malpas, Stuart J. McDonald, William T. O’Brien, Craig Despott, Kelly L. Bertram, Matthew P. Pase, Lukas Frontzkowski, Meng Law, Terence J. O’Brien, Lucy Vivash   +10 more
wiley   +1 more source

Relationship Between Neurologic Symptoms and Signs and FMR1 Genotype in Premutation Carriers

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Fragile X‐associated Tremor/Ataxia Syndrome (FXTAS) is the most severe late‐onset condition caused by a premutation in the FMR1 gene, characterized by expanded CGG triplet repeats of 55–200. Clinical presentations of FXTAS, including gait ataxia, kinetic tremor, cognitive decline, and rare Parkinsonism, are linked to ...
Flora Tassone, Freddy Chafota, Miguel E. Rentería, Randi J. Hagerman, Ellery Santos, Anna Atkinson, Nicholas G. Martin, Elsdon Storey, Danuta Z. Loesch   +8 more
wiley   +1 more source

The adaptive gamma-BSPE kernel density estimation for nonnegative heavy-tailed data

Journal of Innovative Applied Mathematics and Computational Sciences, 2022
In this work, we consider the nonparametric estimation of the probability density function for nonnegative heavy-tailed (HT) data. The objective is first to propose a new estimator that will combine two regions of observations (high and low density ...
Yasmina ZIANE, Nabil Zougab, Smail Adjabi   +2 more
doaj   +4 more sources

Combining kernel estimators in the uniform deconvolution problem [PDF]

, 2010
We construct a density estimator and an estimator of the distribution function in the uniform deconvolution model. The estimators are based on inversion formulas and kernel estimators of the density of the observations and its derivative.
van Es, Bert
core   +2 more sources

Asymptotic Properties of Error Density Estimators in the Two-Phase Linear Regression Model

Stats
This paper investigates kernel estimation of the error density function for the two-phase linear regression model. We derive the asymptotic distributions of residual-based kernel density estimators.
Fuxia Cheng, Lixia Wang
doaj   +1 more source

ESTIMATOR KERNEL DALAM MODEL REGRESI NONPARAMETRIK

Jurnal Matematika, 2012
Analisis regresi nonparametrik merupakan metode pendugaan kurva regresi yang digunakan jika tidak ada informasi sebelumnya te,ntang benttrk kurva regresi atau tidak terikat pada asumsi bentuk fungsi tertentu.
I Komang Gede Sukarsa, I Gusti Ayu Made Srinadi   +1 more
doaj   +1 more source

Kernel Mean Shrinkage Estimators [PDF]

, 2016
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern ...
Fukumizu, Kenji, Gretton, Arthur, Muandet, Krikamol, Schölkopf, Bernhard, Sriperumbudur, Bharath   +4 more
core   +2 more sources

Regularized nonparametric Volterra kernel estimation [PDF]

Automatica, 2017
In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modelled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional ...
Georgios Birpoutsoukis, Anna Marconato, John Lataire, Johan Schoukens   +3 more
openaire   +5 more sources