Dynamic linear model diagnostics
Publication
, Journal Article
Harrison, J; West, M
Published in: Biometrika
December 1, 1991
SUMMARY: In time series analysis using dynamic linear models, retrospective analysis involves the calculation of filtered, or smoothed, distributions for state parameters in the past. We develop and illustrate novel results that are useful in retrospective assessment of the influence of individual observations on such distributions. In particular, new and computationally simple filtering equations are derived for past state parameters based on leaving out one observation at a time, providing dynamic model based versions of methods currently used in standard, static regression diagnostics. © 1991 Biometrika Trust.
Duke Scholars
Published In
Biometrika
DOI
ISSN
0006-3444
Publication Date
December 1, 1991
Volume
78
Issue
4
Start / End Page
797 / 808
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1403 Econometrics
- 0104 Statistics
- 0103 Numerical and Computational Mathematics
Citation
APA
Chicago
ICMJE
MLA
NLM
Harrison, J., & West, M. (1991). Dynamic linear model diagnostics. Biometrika, 78(4), 797–808. https://doi.org/10.1093/biomet/78.4.797
Harrison, J., and M. West. “Dynamic linear model diagnostics.” Biometrika 78, no. 4 (December 1, 1991): 797–808. https://doi.org/10.1093/biomet/78.4.797.
Harrison J, West M. Dynamic linear model diagnostics. Biometrika. 1991 Dec 1;78(4):797–808.
Harrison, J., and M. West. “Dynamic linear model diagnostics.” Biometrika, vol. 78, no. 4, Dec. 1991, pp. 797–808. Scopus, doi:10.1093/biomet/78.4.797.
Harrison J, West M. Dynamic linear model diagnostics. Biometrika. 1991 Dec 1;78(4):797–808.
Published In
Biometrika
DOI
ISSN
0006-3444
Publication Date
December 1, 1991
Volume
78
Issue
4
Start / End Page
797 / 808
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1403 Econometrics
- 0104 Statistics
- 0103 Numerical and Computational Mathematics