Results 51 to 60 of about 120,097 (295)
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 +3 more
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Arthritis Care &Research, EarlyView.Objective Reports have linked both high and low serum uric acid (SUA) levels to adverse health outcomes. This study aimed to establish a reference interval for SUA in older adults and assessed its association with clinically relevant outcomes in relatively healthy, community‐dwelling individuals aged ≥70 years old.
Amanda J. Rickard +15 more
wiley +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 +1 more
doaj +1 more source
Global Polynomial Kernel Hazard Estimation [PDF]
SSRN Electronic Journal, 2000 This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it symptotically reduces bias with unchanged variance.
Nielsen, Jens Perch, Tanggaard, Carsten
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Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2004 Kernel smoothing provides a simple way for finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model and a random design regression model.
Jitka Poměnková
doaj +1 more source
Multivariate kernel density estimation with a parametric support [PDF]
Opuscula Mathematica, 2009 We consider kernel density estimation in the multivariate case, focusing on the use of some elements of parametric estimation. We present a two-step method, based on a modification of the EM algorithm and the generalized kernel density estimator, and ...
Jolanta Jarnicka
doaj +1 more source
Computer Sciences & Mathematics Forum, 2023 In this paper, we propose a non-parametric estimator of the conditional hazard function weighted on the recursive kernel method given an explanatory variable taking values in a semi-metric space when the scalar response is censored.
Hadjer Kebir, Boubaker Mechab
doaj +1 more source
Kernel Estimation of Relative Risk [PDF]
Bernoulli, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kelsall, Julia E., Diggle, Peter J.
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International Journal of Adaptive Control and Signal Processing, EarlyView.This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source