The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Model-order selection
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Cited by
Févotte, C., Bertin, N., & Durrieu, J.-L. (2009) · Neural Computation · doi:10.1162/neco.2008.04-08-771 · Cited by 1,119
Yardibi, T., Li, J., Stoica, P., Xue, M., & Baggeroer, A. B. (2010) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2010.5417172 · Cited by 699
Self-citationA Sparse Image Fusion Algorithm With Application to Pan-Sharpening
Zhu, X. X., & Bamler, R. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2213604 · Cited by 438
Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging
Tan, X., Roberts, W., Li, J., & Stoica, P. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2096218 · Cited by 284
Self-citationBilinear Generalized Approximate Message Passing—Part I: Derivation
Parker, J. T., Schniter, P., & Cevher, V. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2014.2357776 · Cited by 281
Metadata
- Authors
- Stoica, P., & Selen, Y.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2004-07
- Volume
- 21
- Issue
- 4
- Pages
- 36-47
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W1814500939
- Cited by
- 1,358
- References
- 1
- FWCI
- 23.29
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Model-order selection. https://app.cochat.ai/papers/10.1109/msp.2004.1311138
- The paper
- Stoica, P., & Selen, Y. (2004). Model-order selection. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2004.1311138
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
