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- W2089235408 abstract "In this paper a support vector method is proposed for use when the sample observations are contaminated by a normally distributed measurement error. The performance of deconvolution density estimators based on the support vector method is explored and compared with kernel density estimators by means of a simulation study. An interesting result was that for the estimation of kurtotic density, the support vector deconvolution estimator with a Gaussian kernel showed a better performance than the classical deconvolution kernel estimator." @default.
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- W2089235408 date "2012-07-31" @default.
- W2089235408 modified "2023-09-22" @default.
- W2089235408 title "A Note on Deconvolution Estimators when Measurement Errors are Normal" @default.
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- W2089235408 doi "https://doi.org/10.5351/ckss.2012.19.4.517" @default.
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