We propose an automatic selection of the bandwidth of the recursive kernel estimators of a probability density function defined by\nthe stochastic approximation algorithmintroduced byMokkadem et al. (2009a).We showed that, using the selected bandwidth and\nthe stepsizewhich minimize the MISE (mean integrated squared error) of the class of the recursive estimators defined inMokkadem\net al. (2009a), the recursive estimator will be better than the nonrecursive one for small sample setting in terms of estimation error\nand computational costs.We corroborated these theoretical results through simulation study.
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