Multivariate Density Estimation and Visualization [PDF]
, 2011 This chapter examines the use of flexible methods to approximate an unknown density function, and techniques appropriate for visualization of densities in up to four dimensions. The statistical analysis of data is a multilayered endeavor. Data must be carefully examined and cleaned to avoid spurious findings.
Scott, David W.
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LogConcDEAD: An R Package for Maximum Likelihood Estimation of a Multivariate Log-Concave Density [PDF]
Journal of Statistical Software, 2008 In this article we introduce the R package LogConcDEAD (Log-concave density estimation in arbitrary dimensions). Its main function is to compute the nonparametric maximum likelihood estimator of a log-concave density.
Madeleine Cule +2 more
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ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R [PDF]
Journal of Statistical Software, 2007 Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing.
Tarn Duong
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Model-based Methods of Classification: Using the mclust Software in Chemometrics [PDF]
Journal of Statistical Software, 2007 Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a
Chris Fraley, Adrian E. Raftery
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An Improved Model for Kernel Density Estimation Based on Quadtree and Quasi-Interpolation
Mathematics, 2022 There are three main problems for classical kernel density estimation in its application: boundary problem, over-smoothing problem of high (low)-density region and low-efficiency problem of large samples.
Jiecheng Wang, Yantong Liu, Jincai Chang
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Nonparametric density estimation for multivariate bounded data [PDF]
Journal of Statistical Planning and Inference, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Taoufik Bouezmarni, Jeroen V.K. Rombouts
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Fast multivariate log-concave density estimation [PDF]
Computational Statistics & Data Analysis, 2019 A novel computational approach to log-concave density estimation is proposed. Previous approaches utilize the piecewise-affine parametrization of the density induced by the given sample set. The number of parameters as well as non-smooth subgradient-based convex optimization for determining the maximum likelihood density estimate cause long runtimes ...
Fabian Rathke, Christoph Schnörr
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Kernel Density Derivative Estimation of Euler Solutions
Applied Sciences, 2023 Conventional Euler deconvolution is widely used for interpreting profile, grid, and ungridded potential field data. The Tensor Euler deconvolution applies additional constraints to the Euler solution using all gravity vectors and the full gravity ...
Shujin Cao +7 more
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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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Generating VaR Scenarios under Solvency II with Product Beta Distributions
Risks, 2018 We propose a Monte Carlo simulation method to generate stress tests by VaR scenarios under Solvency II for dependent risks on the basis of observed data. This is of particular interest for the construction of Internal Models.
Dietmar Pfeifer, Olena Ragulina
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