Title	Robust Discrimination Designs over Hellinger Neighbourhoods
Creator	Hu, Rui
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2016-09-03T13:59
Description	To aid in the discrimination between two, possibly nonlinear, regression models, we study the construction of experimental designs. Considering that each of these two models might be only approximately specified, robust ``maximin'' designs are proposed. The rough idea is as follows. We impose neighbourhood structures on each regression response, to describe the uncertainty in the specifications of the true underlying models. We determine the least favourable -- in terms of Kullback-Leibler divergence -- members of these neighbourhoods. Optimal designs are those maximizing this minimum divergence. Sequential, adaptive approaches to this maximization are studied. Asymptotic optimality is established.
Extent	27 minutes
Subject	Mathematics; Statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: MacEwan University
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2017-03-04
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0343067
URI	http://hdl.handle.net/2429/60793
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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