Publication:
Density estimation of a mixture distribution with unknown point-mass and normal error
Density estimation of a mixture distribution with unknown point-mass and normal error
| datacite.subject.fos | oecd::Engineering and technology | |
| dc.contributor.author | Dang Duc Trong | |
| dc.contributor.author | Nguyen Hoang Thanh | |
| dc.contributor.author | Nguyen Dang Minh | |
| dc.contributor.author | Nguyen Nhu Lan | |
| dc.date.accessioned | 2022-10-13T03:38:54Z | |
| dc.date.available | 2022-10-13T03:38:54Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | We consider the model Y = X + ξ where Y is observable, ξ is a noise random variable with density fξ , X has an unknown mixed density such that P(X = Xc ) = 1 − p, P(X = a) = p with Xc being continuous and p ∈ (0, 1), a ∈ R. Typically, in the last decade, the model has been widely considered in a number of papers for the case of fully known quantities a, fξ . In this paper, we relax the assumptions and consider the parametric error ξ ∼ σN(0, 1) with an unknown σ > 0. From i.i.d. copies Y1, . . . , Ym of Y we will estimate (σ, p, a, fXc ) where fXc is the density of Xc . We also find the lower bound of convergence rate and verify the minimax property of established estimators. | |
| dc.identifier.doi | 10.1016/j.jspi.2021.04.002 | |
| dc.identifier.uri | http://repository.vlu.edu.vn:443/handle/123456789/253 | |
| dc.language.iso | en_US | |
| dc.relation.ispartof | Journal of Statistical Planning and Inference | |
| dc.relation.issn | 0378-3758 | |
| dc.subject | Deconvolution | |
| dc.subject | Mixture distribution | |
| dc.subject | Inversion problems | |
| dc.subject | Nonparametric estimation | |
| dc.title | Density estimation of a mixture distribution with unknown point-mass and normal error | |
| dc.type | journal-article | |
| dspace.entity.type | Publication | |
| oaire.citation.volume | 215 |