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A semi-parametric additive model for variance heterogeneity

โœ Scribed by R. A. Rigby; D. M. Stasinopoulos

Publisher
Springer US
Year
1996
Tongue
English
Weight
761 KB
Volume
6
Category
Article
ISSN
0960-3174

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โœฆ Synopsis

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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