Results 11 to 20 of about 2,482,864 (253)
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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An orthogonal forward regression technique for sparse kernel density estimation [PDF]
, 2008 Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates ...
Chen, Sheng, Hong, X., Harris, Chris J.
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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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Nonparametric Estimation of Extreme Quantiles with an Application to Longevity Risk
Risks, 2021 A new method to estimate longevity risk based on the kernel estimation of the extreme quantiles of truncated age-at-death distributions is proposed. Its theoretical properties are presented and a simulation study is reported.
Catalina Bolancé, Montserrat Guillen
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Research of comparative analysis of nonparametric density estimation by applying Monte Carlo method
Lietuvos Matematikos Rinkinys, 2013 This paper presents nonparametric statistical estimation of distribution density. The Monte Carlo method is used to show the effects of kernel function for multimodal kernel density estimation.
Indrė Drulytė, Tomas Ruzgas
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Approximate inference of the bandwidth in multivariate kernel density estimation [PDF]
, 2011 Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true ...
Sanguinetti, G. +3 more
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Research Based on High-Dimensional Fused Lasso Partially Linear Model
Mathematics, 2023 In this paper, a partially linear model based on the fused lasso method is proposed to solve the problem of high correlation between adjacent variables, and then the idea of the two-stage estimation method is used to study the solution of this model ...
Aifen Feng +4 more
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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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Entropy, 2008 At the heart of many ICA techniques is a nonparametric estimate of an information measure, usually via nonparametric density estimation, for example, kernel density estimation.
Julian Sorensen
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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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