The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
Abstract
Abstract.We define a subclass of dynamic linear models with unknown hyperpara‐meter called d‐inverse‐gamma models. We then approximate the marginal probability density functions of the hyperparameter and the state vector by the data augmentation algorithm of Tanner and Wong. We prove that the regularity conditions for convergence hold. For practical implementation a forward‐filtering‐backward‐sampling algorithm is suggested, and the relation to Gibbs sampling is discussed in detail.
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
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Forecasting, Structural Time Series Models and the Kalman Filter
Harvey, A. C. (1990) · doi:10.1017/cbo9781107049994 · Cited by 4,195
Cited by
An empirical survey of data augmentation for time series classification with neural networks
Iwana, B. K., & Uchida, S. (2021) · PLOS ONE · doi:10.1371/journal.pone.0254841 · Cited by 725
Statistical algorithms for models in state space using SsfPack 2.2
Koopman, S. J., Shephard, N., & Doornik, J. A. (1999) · The Econometrics Journal · doi:10.1111/1368-423x.00023 · Cited by 466
Fast Sampling of Gaussian Markov Random Fields
Rue, H. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00288 · Cited by 363
Kastner, G., & Frühwirth-Schnatter, S. (2014) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.01.002 · Cited by 287
Self-citationA State-Space Model for National Football League Scores
Glickman, M. E., & Stern, H. S. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10474084 · Cited by 121
Metadata
- Authors
- Frühwirth‐Schnatter, S.
- Venue
- Journal of Time Series Analysis
- Publisher
- Wiley
- Published
- 1994-03
- Volume
- 15
- Issue
- 2
- Pages
- 183-202
- ISSN
- 0143-9782 (ISSN-L)
- 1467-9892
- OpenAlex
- W1985037657
- Cited by
- 808
- References
- 2
- FWCI
- 7.48
- 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: DATA AUGMENTATION AND DYNAMIC LINEAR MODELS. https://app.cochat.ai/papers/10.1111/j.1467-9892.1994.tb00184.x
- The paper
- Frühwirth‐Schnatter, S. (1994). DATA AUGMENTATION AND DYNAMIC LINEAR MODELS. Journal of Time Series Analysis. https://doi.org/10.1111/j.1467-9892.1994.tb00184.x
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.