EconPapers    
Economics at your fingertips  
 

Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design

J.M. McGree

Computational Statistics & Data Analysis, 2017, vol. 113, issue C, 207-225

Abstract: The total entropy utility function is considered for the dual purpose of model discrimination and parameter estimation in Bayesian design. A sequential design setting is considered where it is shown how to efficiently estimate the total entropy utility function in discrete data settings. Utility estimation relies on forming particle approximations to a number of intractable integrals which is afforded by the use of the sequential Monte Carlo algorithm for Bayesian inference. A number of motivating examples are considered for demonstrating the performance of total entropy in comparison to utilities for model discrimination and parameter estimation. The results suggest that the total entropy utility selects designs which are efficient under both experimental goals with little compromise in achieving either goal. As such, for the type of problems considered in this paper, the total entropy utility is advocated as a general utility for Bayesian design in the presence of model uncertainty.

Keywords: Generalized linear models; Generalized nonlinear models; Optimal design; Particle filter; Sequential design; Sequential Monte Carlo (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947316301323
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:113:y:2017:i:c:p:207-225

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Series data maintained by Dana Niculescu ().

 
Page updated 2017-09-29
Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:207-225