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Heteroscedastic Models and an Application to Block Designs

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  • W. Douglas Stirling

Abstract

Models are considered that explain the heteroscedasticity of a response variable as an explicit function either of the response mean or of other explanatory variables. Maximum likelihood parameter estimation assuming normality of the response is shown to be possible with a sequence of weighted least squares calculations and this is illustrated with the analysis of a standard data set. Finally all normal linear models with a random block effect are shown to be transformable into fixed effect heteroscedastic models and this is used to find maximum likelihood estimates in an unbalanced block design.

Suggested Citation

  • W. Douglas Stirling, 1985. "Heteroscedastic Models and an Application to Block Designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(1), pages 33-41, March.
    • Handle: RePEc:bla:jorssc:v:34:y:1985:i:1:p:33-41
    DOI: 10.2307/2347882
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    Cited by:

    1. Tsung-Shan Tsou, 2005. "Inferences of variance function - a parametric robust way," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(8), pages 785-796.

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