On the robustness of efficient experimental designs towards the underlying decision rule

van Cranenburgh, S; Rose, JM; Chorus, CG

On the robustness of efficient experimental designs towards the underlying decision rule

van Cranenburgh, S Rose, JM Chorus, CG

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Publication Type:: Journal Article
Citation:: Transportation Research Part A: Policy and Practice, 2018, 109 pp. 50 - 64
Issue Date:: 2018-03-01

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Field	Value	Language
dc.contributor.author	van Cranenburgh, S	en_US
dc.contributor.author	Rose, JM	en_US
dc.contributor.author	Chorus, CG	en_US
dc.date.issued	2018-03-01	en_US
dc.identifier.citation	Transportation Research Part A: Policy and Practice, 2018, 109 pp. 50 - 64	en_US
dc.identifier.issn	0965-8564	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130373
dc.description.abstract	© 2018 Elsevier Ltd We present a methodology to derive efficient designs for Stated Choice (SC) experiments based on Random Regret Minimisation (RRM) behavioural assumptions. This complements earlier work on the design of efficient SC experiments based on Random Utility Maximisation (RUM) models. Capitalizing on this methodology, and using both analytical derivations and empirical data, we investigate the importance of the analyst's assumption regarding the underlying decision rule used to generate the efficient experimental design. We find that conventional RUM-efficient designs can be statistically highly inefficient in cases where RRM is the better representation of the actual choice behaviour, and vice versa. Furthermore, we present a methodology to construct efficient designs that are robust towards the uncertainty on the side of the analyst regarding the underlying decision rule.	en_US
dc.relation.ispartof	Transportation Research Part A: Policy and Practice	en_US
dc.relation.isbasedon	10.1016/j.tra.2018.01.001	en_US
dc.subject.classification	Logistics & Transportation	en_US
dc.title	On the robustness of efficient experimental designs towards the underlying decision rule	en_US
dc.type	Journal Article
utslib.citation.volume	109	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1507 Transportation and Freight Services	en_US
utslib.for	1205 Urban and Regional Planning	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Strength - BIDA - Business Intelligence and Data Analytics
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	109	en_US

Abstract:

© 2018 Elsevier Ltd We present a methodology to derive efficient designs for Stated Choice (SC) experiments based on Random Regret Minimisation (RRM) behavioural assumptions. This complements earlier work on the design of efficient SC experiments based on Random Utility Maximisation (RUM) models. Capitalizing on this methodology, and using both analytical derivations and empirical data, we investigate the importance of the analyst's assumption regarding the underlying decision rule used to generate the efficient experimental design. We find that conventional RUM-efficient designs can be statistically highly inefficient in cases where RRM is the better representation of the actual choice behaviour, and vice versa. Furthermore, we present a methodology to construct efficient designs that are robust towards the uncertainty on the side of the analyst regarding the underlying decision rule.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/130373