Subset selection based on likelihood from uniform and related populations

Let π1,  π2, ... π be k (>_2) populations. Let  πi (i = 1, 2, ..., k) be characterized by the uniform distributionon (ai, bi), where exactly one of ai and bi is unknown. With unequal sample sizes, suppose that we wish to select arandom-size subset of the populations containing the one withthe...

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Main Author: Chotai, Jayanti
Format: Others
Language:English
Published: Umeå universitet, Matematisk statistik 1979
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-74924
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spelling ndltd-UPSALLA1-oai-DiVA.org-umu-749242013-07-03T04:28:42ZSubset selection based on likelihood from uniform and related populationsengChotai, JayantiUmeå universitet, Matematisk statistikUmeå : Umeå universitet1979Subset selectionlikelihood ratioorder restrictionsuniform distributionLet π1,  π2, ... π be k (>_2) populations. Let  πi (i = 1, 2, ..., k) be characterized by the uniform distributionon (ai, bi), where exactly one of ai and bi is unknown. With unequal sample sizes, suppose that we wish to select arandom-size subset of the populations containing the one withthe smallest value of 0i = bi - ai. Rule Ri selects πi iff a likelihood-based k-dimensional confidence region for the unknown (01,..., 0k) contains at least one point having 0i as its smallest component. A second rule, R, is derived through a likelihood ratio and is equivalent to that of Barr and Rizvi (1966) when the sample sizes are equal. Numerical comparisons are made. The results apply to the larger class of densities g(z; 0i) = M(z)Q(0i) iff a(0i) < z < b(0i). Extensions to the cases when both ai and bi are unknown and when 0max is of interest are i i indicated. digitalisering@umuReportinfo:eu-repo/semantics/reporttexthttp://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-74924Statistical research report, 0348-0399 ; 7application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Subset selection
likelihood ratio
order restrictions
uniform distribution
spellingShingle Subset selection
likelihood ratio
order restrictions
uniform distribution
Chotai, Jayanti
Subset selection based on likelihood from uniform and related populations
description Let π1,  π2, ... π be k (>_2) populations. Let  πi (i = 1, 2, ..., k) be characterized by the uniform distributionon (ai, bi), where exactly one of ai and bi is unknown. With unequal sample sizes, suppose that we wish to select arandom-size subset of the populations containing the one withthe smallest value of 0i = bi - ai. Rule Ri selects πi iff a likelihood-based k-dimensional confidence region for the unknown (01,..., 0k) contains at least one point having 0i as its smallest component. A second rule, R, is derived through a likelihood ratio and is equivalent to that of Barr and Rizvi (1966) when the sample sizes are equal. Numerical comparisons are made. The results apply to the larger class of densities g(z; 0i) = M(z)Q(0i) iff a(0i) < z < b(0i). Extensions to the cases when both ai and bi are unknown and when 0max is of interest are i i indicated. === digitalisering@umu
author Chotai, Jayanti
author_facet Chotai, Jayanti
author_sort Chotai, Jayanti
title Subset selection based on likelihood from uniform and related populations
title_short Subset selection based on likelihood from uniform and related populations
title_full Subset selection based on likelihood from uniform and related populations
title_fullStr Subset selection based on likelihood from uniform and related populations
title_full_unstemmed Subset selection based on likelihood from uniform and related populations
title_sort subset selection based on likelihood from uniform and related populations
publisher Umeå universitet, Matematisk statistik
publishDate 1979
url http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-74924
work_keys_str_mv AT chotaijayanti subsetselectionbasedonlikelihoodfromuniformandrelatedpopulations
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