Mean-based iterative procedures in linear models with general errors and grouped data

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Rivero, Carlos and Valdés Sánchez, Teófilo (2004) Mean-based iterative procedures in linear models with general errors and grouped data. Scandinavian journal of statistics, 31 (3). pp. 469-486. ISSN 0303-6898

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Official URL: http://www.jstor.org/stable/10.2307/4616843




Abstract

We present in this paper iterative estimation procedures, using conditional expectations, to fit linear models when the distributions of the errors are general and the dependent data stem from a finite number of sources, either grouped or non-grouped with different classification criteria. We propose an initial procedure that is inspired by the expectation-maximization (EM) algorithm, although it does not agree with it. The proposed procedure avoids the nested iteration, which implicitly appears in the initial procedure and also in the EM algorithm. The stochastic asymptotic properties of the corresponding estimators are analysed.


Item Type:Article
Uncontrolled Keywords:Censored-data; maximum-likelihood; em algorithm; regression; asymptotic distributions; consistency; expectation-based imputation; grouped data; iterative estimation; linear models; nested iteration
Subjects:Sciences > Mathematics > Mathematical statistics
ID Code:20211
Deposited On:04 Mar 2013 12:31
Last Modified:18 Jul 2018 10:43

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