Robust Likelihood Calculation for Time Series
Abstract
SUMMARY We propose a computationally efficient method for calculating the likelihoods of a time series under many submodels, each of which assumes a patch of outliers or level shifts. We assume a state space representation of the time series model with a Bayesian-type treatment of anomalies. The calculations form the basis for an efficient and robust estimation procedure. The method is also applicable to linear regression with correlated errors and is illustrated with two examples.
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Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118 · see citing works
- Authors
- Taplin, R. H.
- Affiliations
- Murdoch University (AU)
- Corresponding author
- Taplin, Ross H.
- ORCID
- Taplin, Ross H.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1993-09-01
- Volume
- 55
- Issue
- 4
- Pages
- 829-836
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2526715852
- Cited by
- 29
- References
- 0
- Citations by year
- 2025: 1 · 2022: 1 · 2020: 1 · 2019: 1 · 2014: 2 · 2012: 1
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- likelihood computation, time series, outliers, level shifts, state-space model, robust estimation, submodels
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