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- W4301905938 abstract "In this paper we consider the kernel estimators of a distribution function defined by the stochastic approximation algorithm when the observation are contamined by measurement errors. It is well known that this estimators depends heavily on the choice of a smoothing parameter called the bandwidth. We propose a specific second generation plug-in method of the deconvolution kernel distribution estimators defined by the stochastic approximation algorithm. We show that, using the proposed bandwidth selection and the stepsize which minimize the MISE (Mean Integrated Squared Error), the proposed estimator will be better than the classical one for small sample setting when the error variance is controlled by the noise to signal ratio. We corroborate these theoretical results through simulations and a real dataset." @default.
- W4301905938 created "2022-10-06" @default.
- W4301905938 creator A5056052971 @default.
- W4301905938 date "2016-06-25" @default.
- W4301905938 modified "2023-10-17" @default.
- W4301905938 title "Bandwidth selection in deconvolution kernel distribution estimators defined by stochastic approximation method with Laplace errors" @default.
- W4301905938 doi "https://doi.org/10.48550/arxiv.1606.07948" @default.
- W4301905938 hasPublicationYear "2016" @default.
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