RT Journal Article
T1 Mean-based iterative procedures in linear models with general errors and grouped data
A1 Rivero Rodríguez, Carlos
A1 Valdés Sánchez, Teófilo
AB 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.
PB Wiley
SN 0303-6898
YR 2004
FD 2004
LK https://hdl.handle.net/20.500.14352/50493
UL https://hdl.handle.net/20.500.14352/50493
LA eng
DS Docta Complutense
RD 11 abr 2025