Deconvolution for an atomic distribution : rates of convergence

S. Gugushvili, Bert Es, van, P.J.C. Spreij

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Abstract

Let X1, . . . ,Xn be i.i.d. copies of a random variable X = Y + Z, where Xi = Yi + Zi, and Yi and Zi are independent and have the same distribution as Y and Z, respectively. Assume that the random variables Yi’s are unobservable and that Y = UV, where U and V are independent, U has a Bernoulli distribution with probability of success equal to 1 - p and V has a distribution function F with density f. Let the random variable Z have a known distribution with density k. Based on a sample X1, . . . ,Xn, we consider the problem of nonparametric estimation of the density f and the probability p. Our estimators of f and p are constructed via Fourier inversion and kernel smoothing. We derive their convergence rates over suitable functional classes and show that the estimators are rate-optimal.
Original languageEnglish
Place of PublicationEindhoven
PublisherEurandom
Number of pages19
Publication statusPublished - 2010

Publication series

NameReport Eurandom
Volume2010034
ISSN (Print)1389-2355

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