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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Vilnius University Press
2005-12-01
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| Series: | Lietuvos Matematikos Rinkinys |
| Subjects: | |
| Online Access: | https://www.journals.vu.lt/LMR/article/view/30856 |
| _version_ | 1797194457010929664 |
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| author | Tomas Ruzgas Mindaugas Kavaliauskas |
| author_facet | Tomas Ruzgas Mindaugas Kavaliauskas |
| author_sort | Tomas Ruzgas |
| collection | DOAJ |
| description |
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 cluster is included to general Gaussian mixture model.
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| first_indexed | 2024-03-07T15:40:34Z |
| format | Article |
| id | doaj.art-538577a0d10941d1830e4e1c13a0041a |
| institution | Directory Open Access Journal |
| issn | 0132-2818 2335-898X |
| language | English |
| last_indexed | 2024-04-24T05:56:35Z |
| publishDate | 2005-12-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj.art-538577a0d10941d1830e4e1c13a0041a2024-04-23T09:01:16ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2005-12-0145spec.10.15388/LMR.2005.30856Nonparametric density estimation using a multidimensional mixture model of Gaussian distributionsTomas Ruzgas0Mindaugas KavaliauskasInstitute of Mathematics and Informatics 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 cluster is included to general Gaussian mixture model. https://www.journals.vu.lt/LMR/article/view/30856nonparametric density estimationinversion formulacharacteristic function |
| spellingShingle | Tomas Ruzgas Mindaugas Kavaliauskas Nonparametric density estimation using a multidimensional mixture model of Gaussian distributions Lietuvos Matematikos Rinkinys nonparametric density estimation inversion formula characteristic function |
| title | Nonparametric density estimation using a multidimensional mixture model of Gaussian distributions |
| title_full | Nonparametric density estimation using a multidimensional mixture model of Gaussian distributions |
| title_fullStr | Nonparametric density estimation using a multidimensional mixture model of Gaussian distributions |
| title_full_unstemmed | Nonparametric density estimation using a multidimensional mixture model of Gaussian distributions |
| title_short | Nonparametric density estimation using a multidimensional mixture model of Gaussian distributions |
| title_sort | nonparametric density estimation using a multidimensional mixture model of gaussian distributions |
| topic | nonparametric density estimation inversion formula characteristic function |
| url | https://www.journals.vu.lt/LMR/article/view/30856 |
| work_keys_str_mv | AT tomasruzgas nonparametricdensityestimationusingamultidimensionalmixturemodelofgaussiandistributions AT mindaugaskavaliauskas nonparametricdensityestimationusingamultidimensionalmixturemodelofgaussiandistributions |