The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Frequentist Model Average Estimators
Open accessAbstract
The traditional use of model selection methods in practice is to proceed as if the final selected model had been chosen in advance, without acknowledging the additional uncertainty introduced by model selection. This often means underreporting of variability and too optimistic confidence intervals. We build a general large-sample likelihood apparatus in which limiting distributions and risk properties of estimators post-selection as well as of model average estimators are precisely described, also explicitly taking modeling bias into account. This allows a drastic reduction in complexity, as competing model averaging schemes may be developed, discussed, and compared inside a statistical prototype experiment where only a few crucial quantities matter. In particular, we offer a frequentist view on Bayesian model averaging methods and give a link to generalized ridge estimators. Our work also leads to new model selection criteria. The methods are illustrated with real data applications.
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
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Regression and time series model selection in small samples
HURVICH, C. M., & TSAI, C.-L. (1989) · Biometrika · doi:10.1093/biomet/76.2.297 · Cited by 6,586
Approximation Theorems of Mathematical Statistics
Serfling, R. J. (1980) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316481 · Cited by 5,851
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340
Ziegel, E. R., Lehmann, E. L., & Casella, G. (1999) · Technometrics · doi:10.2307/1270597 · Cited by 4,203
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118 · cited 2×
“First, there are dif culties associated with the often ad hoc way in which the prior probabilities for a (sometimes long) list of models is set up; see the discussion to Hoeting et al. (1999).”
BackgroundModel Selection: An Integral Part of Inference
Buckland, S. T., Burnham, K. P., & Augustin, N. H. (1997) · Biometrics · doi:10.2307/2533961 · Cited by 1,717
Assessment and Propagation of Model Uncertainty
Draper, D. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02015.x · Cited by 1,599
Cited by
Generalized additive models for location, scale and shape.
Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228
Hansen, B. E. (2007) · Econometrica · doi:10.1111/j.1468-0262.2007.00785.x · Cited by 837
Cross validation for model selection: A review with examples from ecology
Yates, L. A., Aandahl, Z., Richards, S. A., & Brook, B. W. (2023) · Ecological Monographs · doi:10.1002/ecm.1557 · Cited by 459
Model Averaging and Its Use in Economics
Steel, M. F. J. (2020) · Journal of Economic Literature · doi:10.1257/jel.20191385 · Cited by 321
Horta, A., Malone, B., Stockmann, U., Minasny, B., Bishop, T. F. A., McBratney, A. B., Pallasser, R., & Pozza, L. (2015) · Geoderma · doi:10.1016/j.geoderma.2014.11.024 · Cited by 306
Metadata
- Authors
- Hjort, N. L., & Claeskens, G.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2003-12
- Volume
- 98
- Issue
- 464
- Pages
- 879-899
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2078502317
- Cited by
- 688
- References
- 28
- FWCI
- 13.04
- Type
- journal article
- Language
- en
- Open access
- green
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: Frequentist Model Average Estimators. https://app.cochat.ai/papers/10.1198/016214503000000828
- The paper
- Hjort, N. L., & Claeskens, G. (2003). Frequentist Model Average Estimators. Journal of the American Statistical Association. https://doi.org/10.1198/016214503000000828
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.
