Results 31 to 40 of about 45,420 (307)
Signal Estimation Using Wavelet Analysis of Solution Monitoring Data for Nuclear Safeguards
Axioms, 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]
Journal 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
CoRRUnder the assumption that data lie on a compact (unknown) manifold without boundary, we derive finite sample bounds for kernel smoothing and its (first and second) derivatives, and we establish asymptotic normality through Berry-Esseen type bounds.
Eunseong Bae, Wolfgang Polonik
openaire +2 more sources
Smoothing under Diffeomorphic Constraints with Homeomorphic Splines [PDF]
, 2010 In this paper we introduce a new class of diffeomorphic smoothers based on general spline smoothing techniques and on the use of some tools that have been recently developed in the context of image warping to compute smooth diffeomorphisms.
Gadat, Sébastien, Bigot, Jérémie
core +1 more source
Nonparametric Kernel Smoothing Methods. The sm library in Xlisp-Stat
Journal 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
Infinity, 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
Acta 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
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 +10 more
wiley +1 more source
COMPARATION ON SEVERAL SMOOTHING METHODS IN NONPARAMETRIC REGRESSION [PDF]
, 2011 There are three nonparametric regression methods covered in this section. These are Moving Average Filtering-Based Smoothing, Local Regression Smoothing, and Kernel Smoothing Methods.
Isnanto, R.Rizal, Rizal Isnanto, R
core
Approximate inference of the bandwidth in multivariate kernel density estimation [PDF]
, 2011 Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true ...
Sanguinetti, G. +3 more
core +1 more source