Algorithms for response adaptive sampling designs

Hardwick, Janis; Stout, Quentin F.

Algorithms for response adaptive sampling designs

dc.contributor.author	Hardwick, Janis	en_US
dc.contributor.author	Stout, Quentin F.	en_US
dc.date.accessioned	2009-11-06T16:48:00Z
dc.date.available	2010-03-01T21:10:28Z	en_US
dc.date.issued	2009-07	en_US
dc.identifier.citation	Hardwick, Janis; Stout, Quentin F. (2009). "Algorithms for response adaptive sampling designs." Wiley Interdisciplinary Reviews: Computational Statistics 1(1): 118-122. <http://hdl.handle.net/2027.42/64301>	en_US
dc.identifier.issn	1939-5108	en_US
dc.identifier.issn	1939-0068	en_US
dc.identifier.uri	https://hdl.handle.net/2027.42/64301
dc.description.abstract	An experimental design is a formula or algorithm that specifies how resources are to be utilized throughout a study. The design is considered to be good or even optimal if it allows for sufficiently precise and accurate data analysis with the least output of resources such as time, money and experimental units. Most experimental designs use fixed sampling procedures in which the sample sizes and order of allocations to different study groups are known in advance. Copyright © 2009 John Wiley & Sons, Inc.	en_US
dc.format.extent	114193 bytes
dc.format.extent	3118 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	text/plain
dc.publisher	John Wiley & Sons, Inc.	en_US
dc.subject.other	Computational and Graphical Statistics	en_US
dc.subject.other	Data Mining Statistics	en_US
dc.title	Algorithms for response adaptive sampling designs	en_US
dc.type	Article	en_US
dc.rights.robots	IndexNoFollow	en_US
dc.subject.hlbtoplevel	Science	en_US
dc.description.peerreviewed	Peer Reviewed	en_US
dc.contributor.affiliationum	CSE Department, University of Michigan, Ann Arbor, MI 48109, USA	en_US
dc.contributor.affiliationum	CSE Department, University of Michigan, Ann Arbor, MI 48109, USA ; CSE Department, University of Michigan, Ann Arbor, MI 48109, USA	en_US
dc.description.bitstreamurl	http://deepblue.lib.umich.edu/bitstream/2027.42/64301/1/25_ftp.pdf
dc.identifier.doi	10.1002/wics.25	en_US
dc.identifier.source	Wiley Interdisciplinary Reviews: Computational Statistics	en_US
dc.owningcollname	Interdisciplinary and Peer-Reviewed

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