Multivariate regression model selection from small samples using Kullback's symmetric divergence
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
A note on overfitting properties of KIC and KICc
Signal Processing - Fractional calculus applications in signals and systems
Asymptotic bootstrap corrections of AIC for linear regression models
Signal Processing
MML Invariant Linear Regression
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Adaptive finger angle estimation from sEMG data with multiple linear and nonlinear model data fusion
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Robust control of a prosthetic hand based on a hybrid adaptive finger angle estimation
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
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The Kullback information criterion (KIC) is a recently developed tool for statistical model selection. KIC serves as an asymptotically unbiased estimator of a variant (within a constant) of the Kullback symmetric divergence, known also as J-divergence between the generating model and the fitted candidate model. In this paper, a bias correction to KIC is derived for linear regression models. The correction is of particular use when the sample size is small or when the number of fitted parameters is a moderate to large fraction of the sample size. For linear regression models, the corrected criterion, called KICc is an exactly unbiased estimator of the variant of the Kullback symmetric divergence, assuming that the true model is correctly specified or overfitted. Furthermore, when applied to polynomial regression and autoregressive time-series modeling, KICc is found to estimate the model order more accurately than any other asymptotically efficient method. Finally, KICc is tested on real data to forecast foreign currency exchange rate; the result is very interesting in comparison to classical techniques.