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Variable Kernel Density Estimation
The Annals of Statistics, 1992 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Terrell, George R., Scott, David W.
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Proceedings, 2018 Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed ...
Inés Barbeito, Ricardo Cao
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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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Improved parameter estimation of Time Dependent Kernel Density by using Artificial Neural Networks
Journal of Finance and Data Science, 2018 Time Dependent Kernel Density Estimation (TDKDE) used in modelling time-varying phenomenon requires two input parameters known as bandwidth and discount to perform.
Xing Wang +2 more
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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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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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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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Confidence Intervals for Kernel Density Estimation [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2004 This article describes asciker and bsciker, two programs that enrich the possibility for density analysis using Stata. asciker and bsciker compute asymptotic and bootstrap confidence intervals for kernel density estimation, respectively, based on the theory of kernel density confidence intervals estimation developed in Hall (1992b)and Horowitz (2001 ...
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Kernel Density Estimation for Dynamical Systems
J. Mach. Learn. Res., 2016 sponsorship: The authors are grateful to Professor Laszlo Gyorfi, the reviewers, and the action editor for helpful comments that helped improve the quality and the presentation of this paper. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007 ...
Hang, Hanyuan +3 more
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Do-Validation for Kernel Density Estimation [PDF]
Journal of the American Statistical Association, 2011 Bandwidth selection in kernel density estimation is one of the fundamental model selection problems of mathematical statistics. The study of this problem took major steps forward with the articles of Hall and Marron (1987) and Hall and Johnstone (1992).
Mammen, Enno +3 more
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