On large-sample estimation and testing via quadratic inference functions for correlated data

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dc.creatorPilla, Ramani S.
dc.creatorLoader, Catherine
dc.date2005-05-17
dc.date2006-01-08
dc.date.accessioned2026-07-07T08:06:54Z
dc.date.available2026-07-07T08:06:54Z
dc.descriptionHansen (1982) proposed a class of "generalized method of moments" (GMMs) for estimating a vector of regression parameters from a set of score functions. Hansen established that, under certain regularity conditions, the estimator based on the GMMs is consistent, asymptotically normal and asymptotically efficient. In the generalized estimating equation framework, extending the principle of the GMMs to implicitly estimate the underlying correlation structure leads to a "quadratic inference function" (QIF) for the analysis of correlated data. The main objectives of this research are to (1) formulate an appropriate estimated covariance matrix for the set of extended score functions defining the inference functions; (2) develop a unified large-sample theoretical framework for the QIF; (3) derive a generalization of the QIF test statistic for a general linear hypothesis problem involving correlated data while establishing the asymptotic distribution of the test statistic under the null and local alternative hypotheses; (4) propose an iteratively reweighted generalized least squares algorithm for inference in the QIF framework; and (5) investigate the effect of basis matrices, defining the set of extended score functions, on the size and power of the QIF test through Monte Carlo simulated experiments.
dc.description32 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/math/0505360
dc.identifierhttp://arxiv.org/abs/math/0505360
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130766
dc.subjectStatistics Theory
dc.subject62G10 (Primary) 62H15, 62J12 (Secondary)
dc.titleOn large-sample estimation and testing via quadratic inference functions for correlated data
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