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Model selection through a statistical analysis of the minimum of a weighted least squares cost function
de Brauwere, A.; De Ridder, F.; Pintelon, R.; Elskens, M.; Schoukens, J.; Baeyens, W.F.J. (2005). Model selection through a statistical analysis of the minimum of a weighted least squares cost function. Chemometr. Intell. Lab. Syst. 76(2): 163-173. http://dx.doi.org/10.1016/j.chemolab.2004.10.006
In: Chemometrics and Intelligent Laboratory Systems. Elsevier: Amsterdam; New York; Oxford; Tokyo. ISSN 0169-7439; e-ISSN 1873-3239, more
Peer reviewed article  

Available in  Authors 
    VLIZ: Open Repository 70159 [ OMA ]

Keyword
    Hypothesis testing
Author keywords
    model selection; hypothesis testing; Weighted Least Squares; akaikeinformation criterion; AIC; minimum description length; MDL; BIC

Authors  Top 
  • de Brauwere, A., more
  • De Ridder, F., more
  • Pintelon, R.
  • Elskens, M., more
  • Schoukens, J.
  • Baeyens, W.F.J., more

Abstract
    Combining (i) a statistical interpretation of the minimum of a Weighted Least Squares cost function and (ii) the principle of parsimony,a model selection strategy is proposed. First, it is compared via simulation to model selection methods based on information criteria (AICand MDL type). The first kind of simulations shows that the cost function approach outperforms in selecting the true model, especiallywhen the number of data is very small compared with the number of parameters to be estimated. Next, the model metaselection proposedby de Luna and Skouras [X. De Luna, K. Skouras, Choosing a model selection strategy, Scand. J. Stat. 30(1) (2003) 113-128.] isemployed as an objective method to choose the best model selection method. Applied to one of their examples, clearly the cost functionstrategy is selected as the best method. Finally, on a set of field data, the cost function approach is used for selecting the relevantparameters of a complex model.

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