Journal Article10.1109/MSP.2004.1311138
Model-order selection: a review of information criterion rules
Petre Stoica,Y. Selen +1 more
1.3K
TL;DR: 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 parameter that are equally important for the specification of a data model.
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Abstract: 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 on a sensor array. In each of these cases, the integer-valued parameters determine the dimension of the parameter vector of the data model, and they must be estimated from the data.
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Citations
Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle
TL;DR: A new method for the estimation of multiple concurrent pitches in piano recordings is presented, which addresses the issue of overlapping overtones by modeling the spectral envelope of the overtones of each note with a smooth autoregressive model.
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Model Selection Techniques: An Overview
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Bilinear Generalized Approximate Message Passing—Part I: Derivation
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Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging
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References
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
Gideon Schwarz
- 01 Jan 2005
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
40.6K
•Book
System Identification: Theory for the User
Lennart Ljung
- 01 Jan 1987
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.