White noise

Function (biology)

Algorithm

Bayesian information criterion

Biology

Statistics

Minimum description length

Materials science

Colored

Signal-to-noise ratio (imaging)

Selection (genetic algorithm)

Computer science

Akaike information criterion

Order statistic

Evolutionary biology

Mathematics

Estimator

Image (mathematics)

Extreme value theory

Artificial intelligence

Composite material

Colors of noise

Maximum likelihood

Noise (video)

Model selection

We consider the problem of detecting a number of complex sinusoids in unknown colored noise based on observations from an unstructured array. Based on extreme value theory, we first present a new model complexity penalty term for the log-likelihood function that outperforms both Minimum Description Length (MDL) and Akaike Information Criterion (AIC) in different array sizes. Second, we derive a new signal to noise ratio (SNR) threshold defining the breakdown point associated with the Maximum Likelihood (ML) estimator.

Model Order Selection in Presence of Unknown Colored Noise