Dynamic Generalized Linear Models and Bayesian Forecasting

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 p...

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Veröffentlicht in:	Journal of the American Statistical Association 1985-03, Vol.80 (389), p.73-83
Hauptverfasser:	West, Mike, Harrison, P. Jeff, Migon, Helio S.
Format:	Artikel
Sprache:	eng
Schlagworte:	Binomials Covariance matrices Dynamic regression Exact sciences and technology Exponential family Forecasting models Forecasting standards Generalized linear model Inference from stochastic processes time series analysis Linear Bayes methods Mathematics Modeling Parametric models Probability and statistics Sciences and techniques of general use Statistical forecasts Statistical variance Statistics Theory and Methods Time series forecasting
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