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Article: A predictive approach for the selection of a fixed number of good treatments
Title  A predictive approach for the selection of a fixed number of good treatments 

Authors  
Keywords  Ranking and selection predictive approach correct selection predictive bounds simultaneous control 
Issue Date  1994 
Publisher  Taylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/03610926.asp 
Citation  Communications in Statistics: Theory and Methods, 1994, v. 23 n. 9, p. 24692492 How to Cite? 
Abstract  This paper offers a predictive approach for the selection of a fixed number (= t) of treatments from k treatments with the goal of controlling for predictive losses. For the ith treatment, independent observations X(ij) (j = 1, 2, ..., n) can be observed where X(ij)'s are normally distributed N(theta(i);sigma2). The ranked values of theta(i)'s and X(i)BAR's are theta(1) lessthanorequalto ... lessthanorequalto theta((k)) and X[1]BAR lessthanorequalto ... lessthanorequalto X[k]BAR and the selected subset S = {[k], [k  1], ... , [k  t + 1]) will be considered. This paper distinguishes between two types of loss functions. A type I loss function associated with a selected subset S is the loss in utility from the selector's view point and is a function of theta(i) with i isanelementof S. A type II loss function associated with S measures the unfairness in the selection from candidates' viewpoint and is a function of theta(i) with i isanelementof S. This paper shows that under mild assumptions on the loss functions S is optimal and provides the necessary formulae for choosing n so that the two types of loss can be controlled individually or simultaneously with a high probability. Predictive bounds for the losses are provided. Numerical examples support the usefulness of the predictive approach over the design of experiment approach. 
Persistent Identifier  http://hdl.handle.net/10722/82812 
ISSN  2015 Impact Factor: 0.3 2015 SCImago Journal Rankings: 0.518 
DC Field  Value  Language 

dc.contributor.author  Lam, K  en_HK 
dc.contributor.author  Yu, PLH  en_HK 
dc.date.accessioned  20100906T08:33:42Z   
dc.date.available  20100906T08:33:42Z   
dc.date.issued  1994  en_HK 
dc.identifier.citation  Communications in Statistics: Theory and Methods, 1994, v. 23 n. 9, p. 24692492  en_HK 
dc.identifier.issn  03610926  en_HK 
dc.identifier.uri  http://hdl.handle.net/10722/82812   
dc.description.abstract  This paper offers a predictive approach for the selection of a fixed number (= t) of treatments from k treatments with the goal of controlling for predictive losses. For the ith treatment, independent observations X(ij) (j = 1, 2, ..., n) can be observed where X(ij)'s are normally distributed N(theta(i);sigma2). The ranked values of theta(i)'s and X(i)BAR's are theta(1) lessthanorequalto ... lessthanorequalto theta((k)) and X[1]BAR lessthanorequalto ... lessthanorequalto X[k]BAR and the selected subset S = {[k], [k  1], ... , [k  t + 1]) will be considered. This paper distinguishes between two types of loss functions. A type I loss function associated with a selected subset S is the loss in utility from the selector's view point and is a function of theta(i) with i isanelementof S. A type II loss function associated with S measures the unfairness in the selection from candidates' viewpoint and is a function of theta(i) with i isanelementof S. This paper shows that under mild assumptions on the loss functions S is optimal and provides the necessary formulae for choosing n so that the two types of loss can be controlled individually or simultaneously with a high probability. Predictive bounds for the losses are provided. Numerical examples support the usefulness of the predictive approach over the design of experiment approach.   
dc.language  eng  en_HK 
dc.publisher  Taylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/03610926.asp  en_HK 
dc.relation.ispartof  Communications in Statistics: Theory and Methods  en_HK 
dc.rights  This is an electronic version of an article published in [include the complete citation information for the final version of the article as published in the print edition of the journal]. [JOURNAL TITLE] is available online at: http://www.informaworld.com/smpp/ with the open URL of your article.   
dc.subject  Ranking and selection   
dc.subject  predictive approach   
dc.subject  correct selection   
dc.subject  predictive bounds   
dc.subject  simultaneous control   
dc.title  A predictive approach for the selection of a fixed number of good treatments  en_HK 
dc.type  Article  en_HK 
dc.identifier.openurl  http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=03610926&volume=23&issue=9&spage=2469&epage=2492&date=1994&atitle=A+predictive+approach+for+the+selection+of+a+fixed+number+of+good+treatments  en_HK 
dc.identifier.email  Lam, K: hrntlam@hkucc.hku.hk  en_HK 
dc.identifier.email  Yu, PLH: plhyu@hkucc.hku.hk  en_HK 
dc.identifier.hkuros  8662  en_HK 
dc.identifier.volume  23   
dc.identifier.issue  9   
dc.identifier.spage  2469   
dc.identifier.epage  2492   