Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method
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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2014/739640 |
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| author | Yousri Slaoui |
| author_facet | Yousri Slaoui |
| author_sort | Yousri Slaoui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-48322b23fc0d4876a567fc771ca9c327 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-48322b23fc0d4876a567fc771ca9c3272025-02-03T01:01:14ZengWileyJournal of Probability and Statistics1687-952X1687-95382014-01-01201410.1155/2014/739640739640Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation MethodYousri Slaoui0Université de Poitiers, Laboratoire de Mathématiques et Application, 86962 Futuroscope, Chasseneuil, FranceWe 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.http://dx.doi.org/10.1155/2014/739640 |
| spellingShingle | Yousri Slaoui Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method Journal of Probability and Statistics |
| title | Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method |
| title_full | Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method |
| title_fullStr | Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method |
| title_full_unstemmed | Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method |
| title_short | Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method |
| title_sort | bandwidth selection for recursive kernel density estimators defined by stochastic approximation method |
| url | http://dx.doi.org/10.1155/2014/739640 |
| work_keys_str_mv | AT yousrislaoui bandwidthselectionforrecursivekerneldensityestimatorsdefinedbystochasticapproximationmethod |