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Likelihood analysis of non-Gaussian measurement time series
Abstract
In this paper we provide methods for estimating non-Gaussian time series models. These techniques rely on Markov chain Monte Carlo to carry out simulation smoothing and Bayesian posterior analysis of parameters, and on importance sampling to estimate the likelihood function for classical inference. The time series structure of the models is used to ensure that our simulation algorithms are efficient.
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Cited by
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Forecasting Volatility in Financial Markets: A Review
Poon, S., & Granger, C. W. J. (2003) · Journal of Economic Literature · doi:10.1257/002205103765762743 · Cited by 1,315
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
Single-trial spike trains in parietal cortex reveal discrete steps during decision-making
Latimer, K. W., Yates, J. L., Meister, M. L. R., Huk, A. C., & Pillow, J. W. (2015) · Science · doi:10.1126/science.aaa4056 · Cited by 306
Kastner, G., & Frühwirth-Schnatter, S. (2014) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.01.002 · Cited by 287
Metadata
- Authors
- Shephard, N.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1997-09-01
- Volume
- 84
- Issue
- 3
- Pages
- 653-667
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2025262040
- Cited by
- 671
- References
- 0
- FWCI
- 17.10
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shephard, N. (1997). Likelihood analysis of non-Gaussian measurement time series. Biometrika. https://doi.org/10.1093/biomet/84.3.653
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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.
