Publication:
Deconvolution of ℙ(X<Y) with unknown error distributions

datacite.subject.fos oecd::Natural sciences::Mathematics
dc.contributor.author Cao Xuan Phuong
dc.contributor.author Le Thi Hong Thuy
dc.date.accessioned 2022-11-09T11:21:28Z
dc.date.available 2022-11-09T11:21:28Z
dc.date.issued 2020
dc.description.abstract This paper is devoted to a nonparametric estimation of the probability θ : = ℙ ( X < Y ) , where X, Y are continuous univariate random variables of interest and observed with additional random errors. We focus on the case where the distributions of the random errors are unknown but symmetric around zero and can be estimated from some additional samples. Using deconvolution techniques, we propose an estimator of θ which depends on a regularization parameter. We then establish upper and lower bounds on convergence rate of the estimator under mean squared error when error densities are assumed to be supersmooth.
dc.identifier.doi 10.1080/03610926.2020.1849722
dc.identifier.uri http://repository.vlu.edu.vn:443/handle/123456789/1143
dc.language.iso en_US
dc.relation.ispartof Communications in Statistics - Theory and Methods
dc.relation.issn 0361-0926
dc.relation.issn 1532-415X
dc.subject Deconvolution
dc.subject characteristic function
dc.subject supersmooth errors
dc.title Deconvolution of ℙ(X&lt;Y) with unknown error distributions
dc.type journal-article
dspace.entity.type Publication
oaire.citation.issue 17
oaire.citation.volume 51
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