Kernel density estimation and its application
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 technique produces smooth estimate of the pdf...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2018-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://doi.org/10.1051/itmconf/20182300037 |
| _version_ | 1818942721281228800 |
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| author | Węglarczyk Stanisław |
| author_facet | Węglarczyk Stanisław |
| author_sort | Węglarczyk Stanisław |
| collection | DOAJ |
| description | 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 technique produces smooth estimate of the pdf, uses all sample points' locations and more convincingly suggest multimodality. In its two-dimensional applications, kernel estimation is even better as the 2D histogram requires additionally to define the orientation of 2D bins. Two concepts play fundamental role in kernel estimation: kernel function shape and coefficient of smoothness, of which the latter is crucial to the method. Several real-life examples, both for univariate and bivariate applications, are shown. |
| first_indexed | 2024-12-20T07:15:56Z |
| format | Article |
| id | doaj.art-74a148a01b8e494cbc34766f1fcc4ffd |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-20T07:15:56Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-74a148a01b8e494cbc34766f1fcc4ffd2022-12-21T19:48:47ZengEDP SciencesITM Web of Conferences2271-20972018-01-01230003710.1051/itmconf/20182300037itmconf_sam2018_00037Kernel density estimation and its applicationWęglarczyk Stanisław0Cracow University of Technology, Institute of Water Management and Water EngineeringKernel 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 technique produces smooth estimate of the pdf, uses all sample points' locations and more convincingly suggest multimodality. In its two-dimensional applications, kernel estimation is even better as the 2D histogram requires additionally to define the orientation of 2D bins. Two concepts play fundamental role in kernel estimation: kernel function shape and coefficient of smoothness, of which the latter is crucial to the method. Several real-life examples, both for univariate and bivariate applications, are shown.https://doi.org/10.1051/itmconf/20182300037 |
| spellingShingle | Węglarczyk Stanisław Kernel density estimation and its application ITM Web of Conferences |
| title | Kernel density estimation and its application |
| title_full | Kernel density estimation and its application |
| title_fullStr | Kernel density estimation and its application |
| title_full_unstemmed | Kernel density estimation and its application |
| title_short | Kernel density estimation and its application |
| title_sort | kernel density estimation and its application |
| url | https://doi.org/10.1051/itmconf/20182300037 |
| work_keys_str_mv | AT weglarczykstanisław kerneldensityestimationanditsapplication |