In the business applications where only a few data is observed, statistical models estimated in frequentist framework is not reliable or even not obtainable. Bayesian updating, by calculating subjective probabilities conditional on real observations, could form optimal prediction given some prior belief. Through a demonstration of cash flow prediction example, the Bayesian method and a frequentist method, ordinary least square (OLS) to be specific, are compared. Bayesian model has similar performance as OLS in the example and moreover provides a solution to the situations where OLS is inapplicable. Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814696357_001
In this chapter, we present statistical modelling approaches for predictive tasks in business and sc...
We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable ...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
In the business applications where only a few data is observed, statistical models estimated in freq...
Several different types of non-linear Bayesian forecasting models are contrasted and compared. Each ...
Bayesian predictive methods have a number of advantages over traditional statistical methods. For o...
Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferen...
On a scarce-data customer churn prediction problem, using the tiny differences between the predictio...
Bayesian prediction is analyzed in the I.I.D case. In a search for robust methods we combine non par...
This paper builds on some recent work by the author and Werner Ploberger (1991, 1994) on the develop...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
In this thesis, we first propose a coherent inference model that is obtained by distorting the prior...
We propose and implement a coherent statistical framework for combining theoretical and empirical mo...
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forec...
We employ a statistical criterion (out-of-sample hit rate) and a financial market measure (portfolio...
In this chapter, we present statistical modelling approaches for predictive tasks in business and sc...
We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable ...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
In the business applications where only a few data is observed, statistical models estimated in freq...
Several different types of non-linear Bayesian forecasting models are contrasted and compared. Each ...
Bayesian predictive methods have a number of advantages over traditional statistical methods. For o...
Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferen...
On a scarce-data customer churn prediction problem, using the tiny differences between the predictio...
Bayesian prediction is analyzed in the I.I.D case. In a search for robust methods we combine non par...
This paper builds on some recent work by the author and Werner Ploberger (1991, 1994) on the develop...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
In this thesis, we first propose a coherent inference model that is obtained by distorting the prior...
We propose and implement a coherent statistical framework for combining theoretical and empirical mo...
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forec...
We employ a statistical criterion (out-of-sample hit rate) and a financial market measure (portfolio...
In this chapter, we present statistical modelling approaches for predictive tasks in business and sc...
We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable ...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...