Skip to main content

Dynamic generalized linear models and Bayesian forecasting

Publication ,  Journal Article
West, M; Harrison, PJ; Migon, HS
Published in: Journal of the American Statistical Association
January 1, 1985

Dynamic Bayesian models are developed for application in nonlinear, non-normal time series and regression problems, providing dynamic extensions of standard generalized linear models. A key feature of the analysis is the use of conjugate prior and posterior distributions for the exponential family parameters. This leads to the calculation of closed, standard-form predictive distributions for forecasting and model criticism. The structure of the models depends on the time evolution of underlying state variables, and the feedback of observational information to these variables is achieved using linear Bayesian prediction methods. Data analytic aspects of the models concerning scale parameters and outliers are discussed, and some applications are provided. Dynamic Bayesian models are developed for application in nonlinear, non-normal time series and regression problems, providing dynamic extensions of standard generalized linear models. A key feature of the analysis is the use of conjugate prior and posterior distributions for the exponential family parameters. This leads to the calculation of closed, standard-form predictive distributions for forecasting and model criticism. The structure of the models depends on the time evolution of underlying state variables, and the feedback of observational information to these variables is achieved using linear Bayesian prediction methods. Data analytic aspects of the models concerning scale parameters and outliers are discussed, and some applications are provided. © 1985 Taylor & Francis Group, LLC.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

January 1, 1985

Volume

80

Issue

389

Start / End Page

73 / 83

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
West, M., Harrison, P. J., & Migon, H. S. (1985). Dynamic generalized linear models and Bayesian forecasting. Journal of the American Statistical Association, 80(389), 73–83. https://doi.org/10.1080/01621459.1985.10477131
West, M., P. J. Harrison, and H. S. Migon. “Dynamic generalized linear models and Bayesian forecasting.” Journal of the American Statistical Association 80, no. 389 (January 1, 1985): 73–83. https://doi.org/10.1080/01621459.1985.10477131.
West M, Harrison PJ, Migon HS. Dynamic generalized linear models and Bayesian forecasting. Journal of the American Statistical Association. 1985 Jan 1;80(389):73–83.
West, M., et al. “Dynamic generalized linear models and Bayesian forecasting.” Journal of the American Statistical Association, vol. 80, no. 389, Jan. 1985, pp. 73–83. Scopus, doi:10.1080/01621459.1985.10477131.
West M, Harrison PJ, Migon HS. Dynamic generalized linear models and Bayesian forecasting. Journal of the American Statistical Association. 1985 Jan 1;80(389):73–83.

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

January 1, 1985

Volume

80

Issue

389

Start / End Page

73 / 83

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics