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

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Rivero, Carlos y 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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URL Oficial: http://www.jstor.org/stable/10.2307/4616843


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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.


Tipo de documento:Artículo
Palabras clave:Censored-data; maximum-likelihood; em algorithm; regression; asymptotic distributions; consistency; expectation-based imputation; grouped data; iterative estimation; linear models; nested iteration
Materias:Ciencias > Matemáticas > Estadística matemática
Código ID:20211
Depositado:04 Mar 2013 12:31
Última Modificación:18 Jul 2018 10:43

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