Model Order Selection
Visa Koivunen and Esa Ollila, Department of Signal Processing and Acoustics, School of Electrical Engineering, Aalto University, Finland
Abstract
In this chapter we provide an overview of methods used for model order selection. One looks for the best dimension for a parametric model given a set of observed data. Most widely used model order selection techniques stem either form statistical inference or information theory. Among the commonly used statistical methods are Bayesian Information Criterion (BIC) and generalized likelihood ratio tests (GLRT), whereas the Minimum Description Length (MDL) and Akaike Information Criterion (AIC) have their roots in information and coding theory. We also show that likelihood ...
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