Robustness of narrowband DOA algorithms with respect to signal bandwidth
Signal Processing
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Partial likelihood for online order selection
Signal Processing - Special issue: Information theoretic signal processing
Denoising using local projective subspace methods
Neurocomputing
Model order selection in multi-baseline interferometric radar systems
EURASIP Journal on Applied Signal Processing
Model order selection for short data: an exponential fitting test (EFT)
EURASIP Journal on Applied Signal Processing
Theoretical Analysis and Comparison of Several Criteria on Linear Model Dimension Reduction
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Sinusoidal order estimation using angles between subspaces
EURASIP Journal on Advances in Signal Processing
Statistical performance analysis of MDL source enumeration in array processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Effective channel order estimation based on nullspace structure and exponential fit
IEEE Transactions on Signal Processing
Exploratory matrix factorization for PET image analysis
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
A theoretical investigation of several model selection criteria for dimensionality reduction
Pattern Recognition Letters
Hi-index | 35.69 |
The Akaike (1974) information criterion (AIC) and the minimum description length (MDL) are two well-known criteria for model order selection in the additive white noise case. Our aim is to study the influence on their behavior of a large gap between the signal and the noise eigenvalues and of the noise eigenvalue dispersion. Our results are mostly qualitative and serve to explain the behavior of the AIC and the MDL in some cases of great practical importance. We show that when the noise eigenvalues are not clustered sufficiently closely, then the AIC and the MDL may lead to overmodeling by ignoring an arbitrarily large gap between the signal and the noise eigenvalues. For fixed number of data samples, overmodeling becomes more likely for increasing the dispersion of the noise eigenvalues. For fixed dispersion, overmodeling becomes more likely for increasing the number of data samples. Undermodeling may happen in the cases where the signal and the noise eigenvalues are not well separated and the noise eigenvalues are clustered sufficiently closely. We illustrate our results by using simulations from the effective channel order determination area