Model-order selection: a review of information criterion rules

  title={Model-order selection: a review of information criterion rules},
  author={Petre Stoica and Yngve Sel{\'e}n},
  journal={IEEE Signal Processing Magazine},
The parametric (or model-based) methods of signal processing often require not only the estimation of a vector of real-valued parameters but also the selection of one or several integer-valued parameters that are equally important for the specification of a data model. Examples of these integer-valued parameters of the model include the orders of an autoregressive moving average model, the number of sinusoidal components in a sinusoids-in-noise signal, and the number of source signals impinging… 

The Monte-Carlo Sampling Approach to Model Selection: A Primer [Lecture Notes]

This lecture note introduces several Monte-Carlo sampling-based rules for model selection using the maximum a posteriori (MAP) approach for signal processing applications.

Model selection and comparison for independents sinusoids

Through simulations, it is demonstrated that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance.

Signals detection using short-time correlation and autoregressive modeling

The analysis of the signal as dynamic invariant in respect of the linear shift transformation yields the function of model consistency, which enables to detect short-time and nonstationary wave packets at signal to noise ratio as from -20 dB and above.

Estimating the number of signals with unknown parameters under Gaussian noises

A class of signal parameters for which the widely used maximum likelihood method is useless for estimating the number of signals is described, and it is established, that the amplitude parameters belong to this class.

Parameter estimation of exponential signals: A system identification approach

Aalborg Universitet Model selection and comparison for independents sinusoids

Through simulations, it is demonstrated that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance.

A comparative study of model selection criteria for the number of signals

The authors show that, in simple MUSIC additive white noise model, for small sample size n, WIC performs nearly as well as AlCc and outperforms other criteria, and for moderately large to large n, the authors suggest that WIC may be a practical alternative to any criterion.

Identification of autoregressive moving average systems based on noise compensation in the correlation domain

This study presents a new scheme for the identification of minimum-phase autoregressive moving average (ARMA) systems from noise-corrupted observations. From the autocorrelation function (ACF) of the

New approach to model‐order selection

ELECT A new, direct and practical scheme is proposed for determining the model orders of a system, its signal and disturbance using key properties of Kalman filter (KF). Unlike conventional methods,

M-estimator-based robust estimation of the number of components of a superimposed sinusoidal signal model

The proposed methods, which are robust modifications of the commonly used information theoretic criteria, are based on various M-estimator approaches and are robust with respect to outliers present in the data and heavy-tailed noise.



Finite sample criteria for autoregressive order selection

The special finite sample information criterion and combined information criterion are necessary because of the increase of the variance of the residual energy for higher model orders that has not been accounted for in other criteria.

Order selection for vector autoregressive models

Order-selection criteria for vector autoregressive (AR) modeling are discussed and the combined information criterion (CIC) for vector signals is robust to finite sample effects and has the optimal asymptotic penalty factor.

Automatic spectral analysis with time series models

  • P. Broersen
  • Computer Science
    IEEE Trans. Instrum. Meas.
  • 2002
The increased computational speed and developments in the robustness of algorithms have created the possibility to identify automatically a well-fitting time series model for stochastic data that includes precisely the statistically significant details that are present in the data.

On the Likelihood of a Time Series Model

By asking the log likelihood of a model to be an unbiased estimate of the expectedlog likelihood of the model, a reasonable definition of the likelihood is obtained and this allows us to develop a systematic approach to parametric time series modelling.

Exact and Large Sample ML Techniques for Parameter Estimation and Detection in Array Processing

A vast number of algorithms has appeared in the literature for estimating unknown signal parameters from the measured output of a sensor array based on measurements of the array output.

Arma Model Identification

During the past two decades, considerable progress has been made in statistical time series analysis. The aim of this book is to present a survey of one of the most active areas in this field: the

A new look at the statistical model identification

The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as

The statistical theory of linear systems

Publisher Summary The chapter discusses the development of a rather complete inferential theory for ARMAX models. The first problem in the development is the coordinatization of spaces of such

Multi-model approach to model selection

Some properties of the order of an autoregressive model selected by a generalization of Akaike∘s EPF criterion

SUMMARY The asymptotic distribution of the order of an autoregression selected by a generalization of Akaike's FPE criterion is given. Some of the properties of the distribution are investigated. The