Results 31 to 40 of about 257,461 (188)
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
doaj +1 more source
Using conditional kernel density estimation for wind power density forecasting [PDF]
, 2012 Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind ...
Bremnes J. B. +11 more
core +1 more source
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
doaj +1 more source
Parallel Space-Time Kernel Density Estimation
, 2017 The exponential growth of available data has increased the need for interactive exploratory analysis. Dataset can no longer be understood through manual crawling and simple statistics.
Delmelle, Eric +4 more
core +1 more source
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
doaj +1 more source
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
doaj +1 more source
Fast optimization of Multithreshold Entropy Linear Classifier [PDF]
, 2014 Multithreshold Entropy Linear Classifier (MELC) is a density based model which searches for a linear projection maximizing the Cauchy-Schwarz Divergence of dataset kernel density estimation. Despite its good empirical results, one of its drawbacks is the
Czarnecki, Wojciech Marian +1 more
core +3 more sources
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
doaj +1 more source
Probability density estimation with tunable kernels using orthogonal forward regression [PDF]
, 2009 A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and ...
Chen, S., Harris, Chris J., Hong, Xia
core +1 more source
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
doaj +1 more source