Results 31 to 40 of about 260,346 (290)
Kernel density estimation and its application
ITM Web of Conferences, 2018 Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel
Węglarczyk Stanisław
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Exploring Violent and Property Crime Geographically
Nordic Journal of Studies in Policing, 2021 There are multiple geographical crime prediction techniques to use and comparing different prediction techniques therefore becomes important. In the current study we compared the accuracy (Predictive Accuracy Index) and precision (Recapture Rate Index ...
Maria Camacho Doyle +2 more
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Sparse kernel density estimation technique based on zero-norm constraint [PDF]
, 2010 A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity.
Chen, S, Harris, C J, Hong, Xia
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Kernel density estimation by genetic algorithm
Journal of Statistical Computation and Simulation, 2022 This study proposes a data condensation method for multivariate kernel density estimation by genetic algorithm. First, our proposed algorithm generates multiple subsamples of a given size with replacement from the original sample. The subsamples and their constituting data points are regarded as $\it{chromosome}$ and $\it{gene}$, respectively, in the ...
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A novel method for analysis of offshore sustainable energy systems
Energy Exploration & Exploitation, 2023 In the present research, we have proposed a new adaptive kernel density estimation method formulated on the theory of linear diffusion processes. By examining the calculation results we have found that in the tail region, our proposed new adaptive kernel
Yingguang Wang
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Consistency of the kernel density estimator: a survey [PDF]
Statistical Papers, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wied, Dominik, Weißbach, Rafael
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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 +8 more
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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
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A Berry-Esseen Type Bound in Kernel Density Estimation for Negatively Associated Censored Data
Journal of Applied Mathematics, 2013 We discuss the kernel estimation of a density function based on censored data when the survival and the censoring times form the stationary negatively associated (NA) sequences.
Qunying Wu, Pingyan Chen
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Improving Kernel Methods for Density Estimation in Random Differential Equations Problems
Mathematical and Computational Applications, 2020 Kernel density estimation is a non-parametric method to estimate the probability density function of a random quantity from a finite data sample. The estimator consists of a kernel function and a smoothing parameter called the bandwidth.
Juan Carlos Cortés López +1 more
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