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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Umeå universitet, Matematisk statistik
1979
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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 |
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Subset selection likelihood ratio order restrictions uniform distribution |
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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 |
_version_ |
1716590401554481152 |