Results 21 to 30 of about 46,105 (114)
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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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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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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Journal of Traffic and Transportation Engineering (English ed. Online), 2016 Traffic accident frequency has been decreasing in Japan in recent years. Nevertheless, many accidents still occur on residential roads. Area-wide traffic calming measures including Zone 30, which discourages traffic by setting a speed limit of 30 km/h in
Seiji Hashimoto +5 more
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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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β-divergence loss for the kernel density estimation with bias reduced
Statistical Theory and Related Fields, 2021 In this paper, we investigate the problem of estimating the probability density function. The kernel density estimation with bias reduced is nowadays a standard technique in explorative data analysis, there is still a big dispute on how to assess the ...
Hamza Dhaker +2 more
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Fault Diagnosis of Rolling Bearings Based on EWT and KDEC
Entropy, 2017 This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings.
Mingtao Ge, Jie Wang, Xiangyang Ren
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Asymptotic Properties of Error Density Estimators in the Two-Phase Linear Regression Model
StatsThis 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
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Multivariate mixed kernel density estimators and their application in machine learning for classification of biological objects based on spectral measurements [PDF]
Компьютерная оптика, 2019 A problem of non-parametric multivariate density estimation for machine learning and data augmentation is considered. A new mixed density estimation method based on calculating the convolution of independently obtained kernel density estimates for ...
Alexander Sirota +3 more
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A new family of kernels from the beta polynomial kernels with applications in density estimation
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko +2 more
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