Computing optimal experimental designs with respect to a compound Bayes risk criterion

Radoslav Harman and Maryna Prus

Statistics & Probability Letters, 2018, vol. 137, issue C, 135-141

Abstract: We consider the problem of computing optimal experimental designs with respect to a compound Bayes risk criterion, which includes various specific criteria, such as a linear criterion for prediction in a random coefficient regression model. We prove that this problem can be converted into a problem of constrained A-optimality in an artificial model, which allows us to directly use existing theoretical results and software tools. We demonstrate the application of the proposed method for the optimal design of a random coefficient regression model with respect to an integrated mean squared error criterion.

Keywords: Optimal design of experiments; Compound criterion; Constrained design; Bayes risk; Random coefficient regression; A-optimality (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715218300282
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:stapro:v:137:y:2018:i:c:p:135-141

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2018.01.017

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().