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Analysis of variance with general errors and grouped and non-grouped data: Some iterative algorithms

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  • Anido, Carmen
  • Rivero, Carlos
  • Valdés, Teófilo
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    Abstract

    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 99 (2008)
    Issue (Month): 8 (September)
    Pages: 1544-1573

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    Handle: RePEc:eee:jmvana:v:99:y:2008:i:8:p:1544-1573

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    Related research

    Keywords: 62F10 62F15 Iterative estimation Stochastic approximation ANOVA with grouped or censored data Conditional imputation techniques Consistency Asymptotic distributions;

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    1. An, Mark Yuying, 1995. "Logconcavity versus Logconvexity: A Complete Characterization," Working Papers 95-03, Duke University, Department of Economics.
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