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Conditional expectations and residual analysis for the linear model

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  • John Haslett

Abstract

Recent developments in the stochastic modelling of spatial processes have led to a very considerable impact on almost all areas of data analysis. Generically, these involve stating the model in terms of a series of unordered conditional probability statements. Some key developments over the past 20 years are reviewed from a personal perspective. Special attention is given to the linear model with general covariance structure and some simple and important results are presented. These allow powerful and very general extensions to the concept of a residual. This is illustrated with reference to multivariate data and to a repeated measures analysis. © 1998 John Wiley & Sons, Ltd.

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  • John Haslett, 1997. "Conditional expectations and residual analysis for the linear model," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 13(3‐4), pages 259-268, September.
    • Handle: RePEc:wly:apsmda:v:13:y:1997:i:3-4:p:259-268
    DOI: 10.1002/(SICI)1099-0747(199709/12)13:3/43.0.CO;2-B
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