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Model selection by sequentially normalized least squares

Author

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  • Rissanen, Jorma
  • Roos, Teemu
  • Myllymäki, Petri

Abstract

Model selection by means of the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estimation. We introduce a new criterion based on sequentially minimized squared deviations, which are smaller than both the usual least squares and the squared prediction errors used in PLS. We also prove that our criterion has a probabilistic interpretation as a model which is asymptotically optimal within the given class of distributions by reaching the lower bound on the logarithmic prediction errors, given by the so called stochastic complexity, and approximated by BIC. This holds when the regressor (design) matrix is non-random or determined by the observed data as in AR models. The advantages of the criterion include the fact that it can be evaluated efficiently and exactly, without asymptotic approximations, and importantly, there are no adjustable hyper-parameters, which makes it applicable to both small and large amounts of data.

Suggested Citation

  • Rissanen, Jorma & Roos, Teemu & Myllymäki, Petri, 2010. "Model selection by sequentially normalized least squares," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 839-849, April.
    • Handle: RePEc:eee:jmvana:v:101:y:2010:i:4:p:839-849
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    References listed on IDEAS

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    1. Hansen M. H & Yu B., 2001. "Model Selection and the Principle of Minimum Description Length," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 746-774, June.
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    Cited by:

    1. Jussi Määttä & Daniel F. Schmidt & Teemu Roos, 2016. "Subset Selection in Linear Regression using Sequentially Normalized Least Squares: Asymptotic Theory," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 382-395, June.

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