Model-order selection: a review of information criterion rules

@article{Stoica2004ModelorderSA,
  title={Model-order selection: a review of information criterion rules},
  author={Petre Stoica and Yngve Sel{\'e}n},
  journal={IEEE Signal Processing Magazine},
  year={2004},
  volume={21},
  pages={36-47}
}
  • P. Stoica, Y. Selén
  • Published 1 July 2004
  • Computer Science, Mathematics
  • 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… Expand
Model selection and comparison for independents sinusoids
TLDR
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. Expand
Parameter estimation of exponential signals: A system identification approach
TLDR
The proposed methodology, which is called EASI (Exponential Analysis via System Identification), is shown to have a satisfactory performance for practical data lengths, and this not only for white measurement noise but also in cases with highly correlated noise. Expand
Aalborg Universitet Model selection and comparison for independents sinusoids
In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAPExpand
A comparative study of model selection criteria for the number of signals
The performance of six existing model selection criteria is compared, which are commonly used in time series and regression analysis, when they are applied to the problem of the number of signals inExpand
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 theExpand
A time-domain model-based method for the identification of multi-frequency signal parameters
TLDR
The traditional nonlinear separable estimation has been modified and a new time-domain least-squares method was developed for the identification of multi-frequency signal parameters. Expand
M-estimator-based robust estimation of the number of components of a superimposed sinusoidal signal model
In this paper, we consider the problem of estimating the number of components of a superimposed nonlinear sinusoids model of a signal in the presence of additive noise. We propose and provide aExpand
Analysis window length selection for linear signal models
  • Alper Yazar, Ç. Candan
  • Computer Science
  • 2015 23nd Signal Processing and Communications Applications Conference (SIU)
  • 2015
A method is presented for the selection of analysis window length, or the number of input samples, for linear signal modeling without compromising the model assumptions. It is assumed that the signalExpand
Simultaneous estimation of number of signals and signal parameters of superimposed sinusoidal model: A robust sequential bivariate M-periodogram approach
TLDR
The proposed sequential method is based on a robust bivariate M-periodogram and uses the orthogonal structure of the superimposed sinusoidal model for sequential estimation and can provide robust and efficient estimates of the number of signals and signal parameters. Expand
Model Order Estimation for A Sum of Complex Exponentials
TLDR
A new method is presented for estimating the number of terms in a sum of exponentially damped sinusoids embedded in noise by combining the shift-invariance property of the Hankel matrix associated with the signal with a constraint over its singular values to penalize small order estimations. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 43 REFERENCES
Optimal Choice of AR and MA Parts in Autoregressive Moving Average Models
  • R. Kashyap
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1982
TLDR
The Bayesian method of choosing the best model for a given one-dimensional series among a finite number of candidates belonging to autoregressive, moving average, AR, ARMA, and other families is dealt with. Expand
Finite sample criteria for autoregressive order selection
  • P. Broersen
  • Mathematics, Computer Science
  • IEEE Trans. Signal Process.
  • 2000
TLDR
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. Expand
Order selection for vector autoregressive models
TLDR
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. Expand
Automatic spectral analysis with time series models
  • P. Broersen
  • Computer Science
  • IEEE Trans. Instrum. Meas.
  • 2002
TLDR
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. Expand
On the Likelihood of a Time Series Model
TLDR
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. Expand
Exact and Large Sample ML Techniques for Parameter Estimation and Detection in Array Processing
TLDR
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. Expand
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: theExpand
Asymptotic MAP criteria for model selection
  • P. Djurić
  • Mathematics, Computer Science
  • IEEE Trans. Signal Process.
  • 1998
TLDR
This paper derives maximum a posteriori (MAP) rules for several different families of competing models and obtain forms that are similar to AIC and naive MDL, but for some families, however, it is found that the derived penalties are different. Expand
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 asExpand
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 suchExpand
...
1
2
3
4
5
...