# 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} }

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

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#### References

SHOWING 1-10 OF 43 REFERENCES

Optimal Choice of AR and MA Parts in Autoregressive Moving Average Models

- Computer Science, Medicine
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- 1982

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

- Mathematics, Computer Science
- IEEE Trans. Signal Process.
- 2000

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

- Mathematics, Computer Science
- IEEE Trans. Signal Process.
- 2003

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

- 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. Expand

On the Likelihood of a Time Series Model

- Computer Science
- 1978

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

- Computer Science
- 1993

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

- Engineering
- 1992

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… Expand

Asymptotic MAP criteria for model selection

- Mathematics, Computer Science
- IEEE Trans. Signal Process.
- 1998

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

- Mathematics
- 1974

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… Expand

The statistical theory of linear systems

- Mathematics
- 1988

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… Expand