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Estimation of heavy-tailed probability density function with application to Web data |
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| Authors: | |
| Institution: | (1) Kyiv National University, 64 Kyiv, 01033 Vladimirskaya, Ukraine;(2) Institute of Control Sciences, Russian Academy of Sciences, Profsoyuznay 65, 117997 Moscow, Russia |
| Abstract: | Summary Common non-parametric estimators of a probability density function (PDF) show bad performance for heavy-tailed PDFs. Using a parametric approximation of the true cumulative distribution function (CDF), the transformation-retransformation of the data is explored here as a useful tool for the reliable PDF prediction. The PDF estimators are compared by their capacity to solve a classification problem. Simulation results and an application to Web data analysis are presented, too. |
| Keywords: | Non-parametric density estimation Heavy-tailed density Kernel estimate Polygram Extreme value index Risk of misclassification |
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