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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EDP Sciences
2018-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://doi.org/10.1051/itmconf/20182300037 |
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doaj-74a148a01b8e494cbc34766f1fcc4ffd2021-03-02T10:11:59ZengEDP 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 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Węglarczyk Stanisław |
| spellingShingle |
Węglarczyk Stanisław Kernel density estimation and its application ITM Web of Conferences |
| author_facet |
Węglarczyk Stanisław |
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Węglarczyk Stanisław |
| title |
Kernel density estimation and its application |
| title_short |
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_sort |
kernel density estimation and its application |
| publisher |
EDP Sciences |
| series |
ITM Web of Conferences |
| issn |
2271-2097 |
| publishDate |
2018-01-01 |
| 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. |
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https://doi.org/10.1051/itmconf/20182300037 |
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AT weglarczykstanisław kerneldensityestimationanditsapplication |
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