The asymptotically optimal estimating equation for longitudinal data. Strong Consistency

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dc.creatorBalan, R. M.
dc.creatorDumitrescu, L.
dc.creatorSchiopu-Kratina, I.
dc.date2008-07-14
dc.date.accessioned2026-07-07T09:50:07Z
dc.date.available2026-07-07T09:50:07Z
dc.descriptionIn this article, we introduce a conditional marginal model for longitudinal data, in which the residuals form a martingale difference sequence. This model allows us to consider a rich class of estimating equations, which contains several estimating equations proposed in the literature. A particular sequence of estimating equations in this class contains a random matrix $\mathcal{R}_{i-1}^*(β)$, as a replacement for the ``true'' conditional correlation matrix of the $i$-th individual. Using the approach of [12], we identify some sufficient conditions under which this particular sequence of equations is asymptotically optimal (in our class). In the second part of the article, we identify a second set of conditions, under which we prove the existence and strong consistency of a sequence of estimators of $β$, defined as roots of estimation equations which are martingale transforms (in particular, roots of the sequence of asymptotically optimal equations).
dc.descriptionSubmitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0807.2090
dc.identifierhttp://arxiv.org/abs/0807.2090
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/164833
dc.subjectStatistics Theory
dc.subject62F12 (Primary) 62J12 (Secondary)
dc.titleThe asymptotically optimal estimating equation for longitudinal data. Strong Consistency
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