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Nonlinear predictor models
Author(s) -
Ameen Jamal R. M.
Publication year - 1992
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980110405
Subject(s) - bayesian probability , kalman filter , nonlinear system , computer science , simple (philosophy) , mathematics , algorithm , bayesian inference , linear model , mathematical optimization , econometrics , artificial intelligence , machine learning , philosophy , physics , epistemology , quantum mechanics
A complete dynamic model is introduced within the Bayesian framework. This includes the dynamic linear model and the normal discount Bayesian model as special cases and extends to some well‐known models with nonlinear predictors. A number of practically important models are formulated and simple recurrence formulas, similar to those of Kalman, are used in the sequential estimation of the parameters. Finally, a number of practical examples and applications are given.