Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design
Computational Statistics & Data Analysis, 2017, vol. 113, issue C, 207-225
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)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
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 ().