Bayesian optimal design for non-linear model under non-regularity condition

M. Hooshangifar and H. Talebi

Statistics & Probability Letters, 2021, vol. 169, issue C

Abstract: The common classical approach for finding optimal design is minimizing the variance of an unbiased maximum likelihood estimator (MLE) of parameters. However, under regularity condition, the variance of MLE is approximated by the Cramer–Rao lower bound. In this article, optimal designs are obtained under non-regularity condition in non-linear models. In the Bayesian approach, conditional mutual information is used to propose a new optimality criterion; Bayesian optimal design maximizes the mutual information between the observation and the model parameters. A Bayesian compound criterion is also provided to facilitate the performance comparison of the optimal designs. Finally, the equivalence theorem is given for criterion to allow for checking the optimality of the obtained Bayesian design points.

Keywords: Bayesian optimal design; Compound criteria; Conditional mutual information; Non-linear model; Equivalence theorem (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715220302789
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:169:y:2021:i:c:s0167715220302789

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.2020.108975

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 ().