Abstract
Hierarchical generalized linear models (HGLMs) are developed as a synthesis of (i) generalized linear models (GLMs) (ii) mixed linear models, (iii) joint modelling of mean and dispersion and (iv) modelling of spatial and temporal correlations. Statistical inferences for complicated phenomena can be made from such a HGLM, which is capable of being decomposed into diverse component GLMs, allowing the application of standard GLM procedures to those components, in particular those for model checking.
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Lee, Y., Nelder, J.A. (2000). HGLMs for analysis of correlated non-normal data. In: Bethlehem, J.G., van der Heijden, P.G.M. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57678-2_9
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DOI: https://doi.org/10.1007/978-3-642-57678-2_9
Publisher Name: Physica, Heidelberg
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