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Minimum Description Length Model Selection in Gaussian Regression under Data Constraints

In: Statistical Inference, Econometric Analysis and Matrix Algebra

Author

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  • Erkki P. Liski

    (University of Tampere)

  • Antti Liski

    (Tampere University of Technology)

Abstract

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.

Suggested Citation

  • Erkki P. Liski & Antti Liski, 2009. "Minimum Description Length Model Selection in Gaussian Regression under Data Constraints," Springer Books, in: Bernhard Schipp & Walter Kräer (ed.), Statistical Inference, Econometric Analysis and Matrix Algebra, pages 201-208, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2121-5_14
    DOI: 10.1007/978-3-7908-2121-5_14
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