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EXPERIMENTAL DESIGN FOR DEPENDENT DATA

Part of: Design of experiments Sequential methods

Published online by Cambridge University Press:  09 November 2020

S. G. J. SENARATHNE Open the ORCID record for S. G. J. SENARATHNE [Opens in a new window]
S. G. J. SENARATHNE*
Affiliation:
School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia
*
e-mail: [email protected]
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Abstract

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Keywords

adaptive designLaplace approximationmodel discriminationparameter estimationpredictionsequential Monte Carlototal entropy

MSC classification

Primary: 62K05: Optimal designs
Secondary: 62L05: Sequential design 62L12: Sequential estimation
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 104 , Issue 1 , August 2021 , pp. 171 - 172
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Copyright
© 2020 Australian Mathematical Publishing Association Inc.

Footnotes

Thesis submitted to Queensland University of Technology in February 2020; degree approved on 26 June 2020; supervisors James McGree and Christopher Drovandi.

References

Senarathne, S. G. J., Drovandi, C. C. and McGree, J. M., ‘Bayesian sequential design for Copula models’, TEST 29(2) (2019), 454–478.CrossRefGoogle Scholar
Senarathne, S. G. J., Drovandi, C. C. and McGree, J. M., ‘A Laplace-based algorithm for Bayesian adaptive design’, Stat. Comput. 30 (2020), 1183–1208.CrossRefGoogle Scholar
Senarathne, S. G. J., Overstall, A. M. and McGree, J. M., ‘Bayesian adaptive $n$ -of-1 trials for estimating population and individual treatment effects’, Stat. Med., to appear.Google Scholar