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Generalized exponential growth models a bayesian approach
Author(s) -
Migon Helio S.,
Gamerman Dani
Publication year - 1993
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.3980120704
Subject(s) - gompertz function , exponential function , class (philosophy) , exponential family , bayesian probability , mathematics , econometrics , variance (accounting) , computer science , exponential growth , statistics , artificial intelligence , economics , mathematical analysis , accounting
A broad class of normal and non‐normal models for processes with non‐negative and non‐decreasing mean function is presented. This class is called exponential growth models and the inferential procedure is based on dynamic Bayesian forecasting techniques. The aim is to produce the analysis on the original variable avoiding transformation and giving to the practitioner the opportunity to communicate easily with the model. This class of models includes the well‐known exponential, logistic and Gompertz models. Models for counting data are compared with the Normal models using the appropriate variance law. In the examples, the novel aspects of this class of models are illustrated showing an improved performance over simple, standard linear models.