Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method
| Autor: | Yousri Slaoui |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Journal of Probability and Statistics, Vol 2014 (2014) |
| Druh dokumentu: | article |
| ISSN: | 1687-952X 1687-9538 |
| DOI: | 10.1155/2014/739640 |
| Popis: | We propose an automatic selection of the bandwidth of the recursive kernel estimators of a probability density function defined by the stochastic approximation algorithm introduced by Mokkadem et al. (2009a). We showed that, using the selected bandwidth and the stepsize which minimize the MISE (mean integrated squared error) of the class of the recursive estimators defined in Mokkadem et al. (2009a), the recursive estimator will be better than the nonrecursive one for small sample setting in terms of estimation error and computational costs. We corroborated these theoretical results through simulation study. |
| Databáze: | Directory of Open Access Journals |
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