The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
The simulation smoother for time series models
Abstract
Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding the degeneracies inherent in state samplers. The technique is important in Gibbs sampling with non-Gaussian time series models, and for performing Bayesian analysis of Gaussian time series.
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Cites
Ripley, B. D. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316726 · Cited by 2,241
On Gibbs sampling for state space models
CARTER, C. K., & KOHN, R. (1994) · Biometrika · doi:10.1093/biomet/81.3.541 · Cited by 1,917
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713
Multivariate Stochastic Variance Models
Harvey, A., Ruiz, E., & Shephard, N. (1994) · The Review of Economic Studies · doi:10.2307/2297980 · Cited by 1,364
Self-citationBayesian Analysis of Stochastic Volatility Models
Jacquier, E., Polson, N. G., & Rossi, P. E. (2002) · Journal of Business & Economic Statistics · doi:10.1198/073500102753410408 · Cited by 1,362
DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
Frühwirth‐Schnatter, S. (1994) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.1994.tb00184.x · Cited by 808
Pricing foreign currency options with stochastic volatility
Melino, A., & Turnbull, S. M. (1990) · Journal of Econometrics · doi:10.1016/0304-4076(90)90100-8 · Cited by 748
A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling
Carlin, B. P., Polson, N. G., & Stoffer, D. S. (1992) · Journal of the American Statistical Association · doi:10.1080/01621459.1992.10475231 · Cited by 562
LIU, J. S., WONG, W. H., & KONG, A. (1994) · Biometrika · doi:10.1093/biomet/81.1.27 · Cited by 542
Albert, J. H., & Chib, S. (1993) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1993.10509929 · Cited by 436
Cited by
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Inferring causal impact using Bayesian structural time-series models
Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015) · The Annals of Applied Statistics · doi:10.1214/14-aoas788 · Cited by 961
Monte Carlo Smoothing for Nonlinear Time Series
Godsill, S. J., Doucet, A., & West, M. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000000151 · Cited by 543
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
Self-citationStochastic volatility with leverage: Fast and efficient likelihood inference
Omori, Y., Chib, S., Shephard, N., & Nakajima, J. (2007) · Journal of Econometrics · doi:10.1016/j.jeconom.2006.07.008 · Cited by 462
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Metadata
- Authors
- DE JONG, P., & SHEPHARD, N.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1995
- Volume
- 82
- Issue
- 2
- Pages
- 339-350
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W1994823593
- Cited by
- 566
- References
- 20
- FWCI
- 13.02
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
