A note on deconvolution density estimation

Prakash Patil

Statistics & Probability Letters, 1996, vol. 29, issue 1, 79-84

Abstract: We consider the problem of estimating the unknown common density f of unobservable independent random variables Xi from observable independent random variables Yij, where conditional density of Yij given Xi, is of the form gYijXi(y) = g(y - Xi; = 1,...,n and J = 1,...,m. It is shown that if E[Y2ijXi] is finite and m grows sufficiently fast then mean square error of the kernel estimator of f converges to zero with usual rate of nonparametric density estimators based on a random sample from a density that is to be estimated. This is in contrast to the case of fixed m.

Keywords: Mean; square; error; Mixing; density; Multiple; observations; Noise; level; Nonparametric; density; estimation (search for similar items in EconPapers)
Date: 1996
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