Mean-Based Iterative Procedures in Linear Models with General Errors and Grouped Data

Authors: Carlos Rivero; Teófilo Valdés

Source: Scandinavian Journal of Statistics, Volume 31, Number 3, September 2004 , pp. 469-486(18)

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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.

Keywords: asymptotic distributions; consistency; expectation-based imputation; grouped data; iterative estimation; linear models; nested iteration

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9469.2004.01_108.x

Affiliations: 1: Universidad Complutense de Madrid

Publication date: 2004-09-01