2026-10-30T00:17:35Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/504862025-02-13T14:56:53Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Anido, Carmen
author
Rivero Rodríguez, Carlos
author
Valdés Sánchez, Teófilo
2023-06-20T10:33:18Z
2023-06-20T10:33:18Z
2008
0047-259X
10.1016/j.jmva.2008.01.003
https://hdl.handle.net/20.500.14352/50486
http://www.sciencedirect.com/science/article/pii/S0047259X08000146
http://www.sciencedirect.com/
In this paper we consider some iterative estimation algorithms, which are valid to analyse the variance of data, which may be either non-grouped or grouped with different classification intervals. This situation appears, for instance, when data is collected from different sources and the grouping intervals differ from one source to another. The analysis of variance is carried out by means of general linear models, whose error terms may be general. An initial procedure in the line of the EM, although it does not necessarily agree with it, opens the paper and gives rise to a simplified version where we avoid the double iteration, which implicitly appears in the EM and, also, in the initial procedure mentioned above. The asymptotic stochastic properties of the resulting estimates have been investigated in depth and used to test ANOVA hypothesis
eng
Analysis of variance with general errors and grouped and non-grouped data: Some iterative algorithms
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https://docta.ucm.es/bitstreams/34868203-c3e8-427e-b87c-40a9cb0b80e1/download
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Teofilo05elsevier.pdf
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83280
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Teofilo05elsevier.pdf.txt