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- W3004641814 abstract "Let X1, . . . ,Xn be i.i.d. observations, where Xi = Yi+snZi and the Y ’s and Z’s are independent. Assume that the Y ’s are unobservable and that they have the density f and also that the Z’s have a known density k. Furthermore, let sn depend on n and let sn ? 0 as n ? 8. We consider the deconvolution problem, i.e. the problem of estimation of the density f based on the sample X1, . . . ,Xn. A popular estimator of f in this setting is the deconvolution kernel density estimator. We derive its asymptotic normality under two different assumptions on the relation between the sequence sn and the sequence of bandwidths hn. We also consider several simulation examples which illustrate different types of asymptotics corresponding to the derived theoretical results and which show that there exist situations where models with sn ? 0 have to be preferred to the models with fixed s." @default.
- W3004641814 created "2020-02-14" @default.
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- W3004641814 date "2009-01-01" @default.
- W3004641814 modified "2023-09-23" @default.
- W3004641814 title "Asymptotic normality of the deconvolution kernel density estimator under the vanishing error variance" @default.
- W3004641814 hasPublicationYear "2009" @default.
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