Nonparametric density estimation using a multidimensional mixture model of Gaussian distributions
This paper algorithmically and empirically studies five major types of nonparametric multivariate density estimation techniques, where no assumption is made about data being drawn from any of known parametric families of distribution. There is developed method of inversion formula where noise clust...
Saved in:
Main Authors: | Tomas Ruzgas, Mindaugas Kavaliauskas |
---|---|
Format: | Article |
Language: | English |
Published: |
Vilnius University Press
2005-12-01
|
Series: | Lietuvos Matematikos Rinkinys |
Subjects: | |
Online Access: | https://www.journals.vu.lt/LMR/article/view/30856 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research of nonparametric density estimation algorithms by applying clustering methods
by: Rasa Šmidtaitė, et al.
Published: (2023-09-01) -
Modeling and simulation of some cell dispersion problems by a nonparametric method
by: Christina Surulescu, et al.
Published: (2011-03-01) -
Data Analytics and Distribution Function Estimation via Mean Absolute Deviation: Nonparametric Approach
by: Elsayed A. H. Elamir
Published: (2025-02-01) -
Nonparametric estimation of the number of classes with different average brightness in thermal images
by: A.N. Galyntich, et al.
Published: (2023-10-01) -
Nonparametric spatio-temporal modeling: Contruction of a geographically and temporally weighted spline regression
by: Sifriyani, et al.
Published: (2025-06-01)