The applicability of biased estimation in model and model order selection
Alkhaldi, Weaam, Iskander, Robert, & Zoubir, Abdelhak M. (2009) The applicability of biased estimation in model and model order selection. In IEEE - Signal Processing Magazine, IEEE, Taipei International Convention Center, Taipei, Taiwan, pp. 3461-3464.
Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance of the minimum description length (MDL) criterion based on biased and unbiased estimation and compare it against modern model selection criteria such as Kay's conditional model order estimator (CME), the bootstrap and the more recently proposed hook-and-loop resampling based model selection. The advantages and limitations of the considered techniques are discussed. The results indicate that, in some cases, biased estimators can slightly improve the selection of the correct model. We also give an example for which the CME with an unbiased estimator fails, but could regain its power when a biased estimator is used.
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|Item Type:||Conference Paper|
|Keywords:||biased estimation, model selection, model order estimation, bootstrap|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Optometry & Vision Science
|Deposited On:||16 Mar 2010 22:36|
|Last Modified:||08 Jun 2016 05:55|
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