Results 271 to 280 of about 52,951 (307)
Some of the next articles are maybe not open access.
The locally Gaussian density estimator for multivariate data
Statistics and Computing, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Håkon Otneim, Dag Tjøstheim
openaire +2 more sources
Multivariate Density Estimation
1996Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.
openaire +1 more source
L p-consistency of multivariate density estimates
Annals of the Institute of Statistical Mathematics, 1982L p notion of the weak, mean, and strong consistency of the kernel method of multivariate density estimation is proposed and studied. The results expand, unify, or generalize most known results in the literature. Rates of convergence in mean and strongL p-consistencies are presented.
openaire +1 more source
Multivariate Visualization by Density Estimation
2008Density estimation and related methods provide a powerful set of tools for visualization of data-based distributions in one, two, and higher dimensions. This chapter examines a variety of such estimators, as well as the various issues related to their theoretical quality and practical application.
Michael C. Minnotte +2 more
openaire +1 more source
Parsimonious multivariate copula model for density estimation
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013The most common approaches for estimating multivariate density assume a parametric form for the joint distribution. The choice of this parametric form imposes constraints on the marginal distributions. Copula models disentangle the choice of marginals from the joint distributions, making it a powerful model for multivariate density estimation. However,
Alireza Bayestehtashk, Izhak Shafran
openaire +1 more source
Feature significance for multivariate kernel density estimation
Computational Statistics & Data Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tarn Duong +3 more
openaire +3 more sources
Nonparametric multivariate density estimation using mixtures
Statistics and Computing, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xuxu Wang, Yong Wang 0049
openaire +2 more sources
Nonparametric multivariate density estimation: a comparative study
IEEE Transactions on Signal Processing, 1994The paper algorithmically and empirically studies two major types of nonparametric multivariate density estimation techniques, where no assumption is made about the data being drawn from any of known parametric families of distribution. The first type is the popular kernel method (and several of its variants) which uses locally tuned radial basis (e.g.,
Jenq-Neng Hwang +2 more
openaire +1 more source
Multivariate density estimation with optimal marginal parzen density estimation and gaussianization
Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004., 2005Multivariate density estimation is an important problem that is frequently encountered in statistical learning and signal processing. One of the most popular techniques is Parzen windowing, also referred to as kernel density estimation. Gaussianization is a procedure that allows one to estimate multivariate densities efficiently from the marginal ...
D. Erdogmus +3 more
openaire +1 more source