Abstract
This paper presents a flexible model for variance heterogeneity in a normal error model. Specifically, both the mean and variance are modelled using semi-parametric additive models. We call this model a ‘Mean And Dispersion Additive Model’ (MADAM). A successive relaxation algorithm for fitting the model is described and justified as maximizing a penalized likelihood function with penalties for lack of smoothness in the additive non-parametric functions in both mean and variance models. The algorithm is implemented in GLIM4, allowing flexible and interactive modelling of variance heterogeneity. Two data sets are used for demonstration.
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Rigby, R.A., Stasinopoulos, D.M. A semi-parametric additive model for variance heterogeneity. Stat Comput 6, 57–65 (1996). https://doi.org/10.1007/BF00161574
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DOI: https://doi.org/10.1007/BF00161574