Add to ORKGSeveral different types of non-linear Bayesian forecasting models are contrasted and compared. Each is shown capable of producing coherent forecasting methodologies. A set of desirable properties is presented to help guide the practitioner in a choice of an appropriate forecasting scheme for his particular application. Some characterizations and impossibility theorems are then given. The paper ends by illustrating how to adapt the Dynamic Generalized Linear Model to make it coherent
This paper builds on some recent work by the author and Werner Ploberger (1991, 1994) on the develop...
Although computer models are often used for forecasting future outcomes of complex systems, the unce...
Computer models may have functional outputs. With no loss of generality, we as-sume that a single co...
Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferen...
We present the results on the comparison of efficiency of approximate Bayesian methods for the analy...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
This thesis is devoted to the analysis and modelling of time series and it is concentrated on models...
In this paper the relative forecast performance of nonlinear models to linear models is assessed by ...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
A non-linear geometric combination of statistical models is proposed as an alternative approach to t...
In the business applications where only a few data is observed, statistical models estimated in freq...
In the early 70's, Harrison and Stevens made a major contribution to the area of statistical foreca...
A nonlinear geometric combination of statistical models is proposed as an alternative approach to th...
It is generally considered that the statistical forecasting methods are superior to the methods whic...
In the literature, many statistical models have been used to investigate the existence of a determin...
This paper builds on some recent work by the author and Werner Ploberger (1991, 1994) on the develop...
Although computer models are often used for forecasting future outcomes of complex systems, the unce...
Computer models may have functional outputs. With no loss of generality, we as-sume that a single co...
Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferen...
We present the results on the comparison of efficiency of approximate Bayesian methods for the analy...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
This thesis is devoted to the analysis and modelling of time series and it is concentrated on models...
In this paper the relative forecast performance of nonlinear models to linear models is assessed by ...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
A non-linear geometric combination of statistical models is proposed as an alternative approach to t...
In the business applications where only a few data is observed, statistical models estimated in freq...
In the early 70's, Harrison and Stevens made a major contribution to the area of statistical foreca...
A nonlinear geometric combination of statistical models is proposed as an alternative approach to th...
It is generally considered that the statistical forecasting methods are superior to the methods whic...
In the literature, many statistical models have been used to investigate the existence of a determin...
This paper builds on some recent work by the author and Werner Ploberger (1991, 1994) on the develop...
Although computer models are often used for forecasting future outcomes of complex systems, the unce...
Computer models may have functional outputs. With no loss of generality, we as-sume that a single co...