Estimation of sums of random variables: Examples and information bounds

dc.creatorZhang, Cun-Hui
dc.date2006-02-10
dc.date.accessioned2026-07-07T08:07:31Z
dc.date.available2026-07-07T08:07:31Z
dc.descriptionThis paper concerns the estimation of sums of functions of observable and unobservable variables. Lower bounds for the asymptotic variance and a convolution theorem are derived in general finite- and infinite-dimensional models. An explicit relationship is established between efficient influence functions for the estimation of sums of variables and the estimation of their means. Certain ``plug-in'' estimators are proved to be asymptotically efficient in finite-dimensional models, while ``$u,v$'' estimators of Robbins are proved to be efficient in infinite-dimensional mixture models. Examples include certain species, network and data confidentiality problems.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053605000000390 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0602214
dc.identifierhttp://arxiv.org/abs/math/0602214
dc.identifierAnnals of Statistics 2005, Vol. 33, No. 5, 2022-2041
dc.identifierdoi:10.1214/009053605000000390
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130960
dc.subjectStatistics Theory
dc.subject62F10, 62F12, 62G05, 62G20 (Primary) 62F15 (Secondary)
dc.titleEstimation of sums of random variables: Examples and information bounds
dc.typetext

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