Results 31 to 40 of about 665,590 (295)
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
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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
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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
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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
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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
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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
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Heat Kernels, Smoothness Estimates, and Exponential Decay [PDF]
Journal of Fourier Analysis and Applications, 2012 25 ...
Boggess, Albert, Raich, Andrew
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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
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FEBS Open Bio, EarlyView.KDAC6 has been associated with cell motility and actin structures, but specific domain contributions are not established. The two catalytic domains have differential effects on motility and F‐actin regulation, affecting cortical F‐actin density, motility, stress fibers, and cell spreading.
Taylor V. Joseph +7 more
wiley +1 more source
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
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