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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Minimum Description Length Model Selection in Gaussian Regression under Data Constraints
verfasst von : Erkki P. Liski, Antti Liski
Erschienen in: Statistical Inference, Econometric Analysis and Matrix Algebra
Verlag: Physica-Verlag HD
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The normalized maximum likelihood (NML) formulation of the stochastic complexity Rissanen ([10]) contains two components: the maximized log likelihood and a component that may be interpreted as the parametric complexity of the model. The stochastic complexity for the data, relative to a suggested model, serves as a criterion for model selection. The calculation of the stochastic complexity can be considered as an implementation of the minimum description length principle (MDL) (cf. Rissanen [12]). To obtain an
NML
based model selection criterion for the Gaussian linear regression, Rissanen [11] constrains the data space appropriately. In this paper we demonstrate the effect of the data constraints on the selection criterion. In fact, we obtain various forms of the criterion by reformulating the shape of the data constraints. A special emphasis is placed on the performance of the criterion when collinearity is present in data.