Technical Program

Paper Detail

Session:System Identification and Parameter Estimation
Time:Tuesday, May 18, 13:00 - 15:00
Presentation: Poster
Topic: Signal Processing Theory and Methods: System Modeling, Representation, & Identification
Authors: Maiza Bekara; École Supérieure d'Electricité (Supelec) 
 Gilles Fleury; École Supérieure d'Electricité (Supelec) 
Abstract: The Kullback Information Criterion KIC and the bias corrected version, KICc are two methods for statistical model selection of regression variables and autoregressive models. Both criteria may be viewed as estimators of the Kullback symmetric divergence between the true model and the fitted approximating model. The bias of KIC and KICc is studied in the underfitting case, where none of the candidate models includes the true model. Here, only normal linear regression models are considered, where exact expression of the bias is obtained for KIC and KICc. The bias of KICc is often smaller, in most case drastically smaller than KIC. A simulation study in which the true model is of infinite order polynomial expansion shows that in small and moderate sample size KICc provides a better model selection than KIC. Furthermore KICc outperforms the two well-known criteria AIC and MDL.

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