{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/92928"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/92928","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables","abstract":"We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals for the method are constructed by leveraging approximations to the total number of structures (tables, multigraphs, or high-dimensional tables), based on results in the literature. The methods generate structures that are very close to the target uniform distribution. Along with their importance weights, the data structures are used to approximate the null distribution of test statistics. In the case of contingency tables, we apply the methods to a number of applications and demonstrate an improvement over competing methods. For loopless, undirected multigraphs, we apply the method to ecological and security problems, and demonstrate excellent performance. In the case of high-dimensional tables, we apply the sequential importance sampling method to the analysis of multimarker linkage disequilibrium data and also demonstrate excellent performance.","abstract_html":"We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals for the method are constructed by leveraging approximations to the total number of structures (tables, multigraphs, or high-dimensional tables), based on results in the literature. The methods generate structures that are very close to the target uniform distribution. Along with their importance weights, the data structures are used to approximate the null distribution of test statistics. In the case of contingency tables, we apply the methods to a number of applications and demonstrate an improvement over competing methods. For loopless, undirected multigraphs, we apply the method to ecological and security problems, and demonstrate excellent performance. In the case of high-dimensional tables, we apply the sequential importance sampling method to the analysis of multimarker linkage disequilibrium data and also demonstrate excellent performance.","abstract_has_math":false,"creators":["Eisinger, Robert David"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Chen, Yuguo","Culpepper, Steven A.","Marden, John I.","Simpson, Douglas G."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T18:27:42Z","date_published":"2016-11-10T18:27:42Z","updated_at":"2026-07-22T22:26:35Z","subjects":["Monte Carlo method","Sequential importance sampling","Counting problem","Contingency Table"],"languages":["en"],"rights":["Copyright 2016 Robert Eisinger"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/92928","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Yuguo","Culpepper, Steven A.","Marden, John I.","Simpson, Douglas G."]},{"key":"dc:creator","label":"Author","values":["Eisinger, Robert David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T18:27:42Z","2018-11-11T10:15:28Z","2016-07-08","2016-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Monte Carlo method","Sequential importance sampling","Counting problem","Contingency Table"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Robert Eisinger"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/92928"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals for the method are constructed by leveraging approximations to the total number of structures (tables, multigraphs, or high-dimensional tables), based on results in the literature. The methods generate structures that are very close to the target uniform distribution. Along with their importance weights, the data structures are used to approximate the null distribution of test statistics. In the case of contingency tables, we apply the methods to a number of applications and demonstrate an improvement over competing methods. For loopless, undirected multigraphs, we apply the method to ecological and security problems, and demonstrate excellent performance. In the case of high-dimensional tables, we apply the sequential importance sampling method to the analysis of multimarker linkage disequilibrium data and also demonstrate excellent performance.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-08-01","The student, Robert Eisinger, accepted the attached license on 2016-07-07 at 10:35.","The student, Robert Eisinger, submitted this Dissertation for approval on 2016-07-07 at 10:48.","This Dissertation was approved for publication on 2016-07-08 at 09:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9792 on 2016-11-10 at 12:20:21","Made available in DSpace on 2016-11-10T18:27:42Z (GMT). No. of bitstreams: 2 EISINGER-DISSERTATION-2016.pdf: 1041551 bytes, checksum: 99a90bbf6d8dde71d46b47903505008b (MD5) LICENSE.txt: 4212 bytes, checksum: c5da0c98593853addf8d27f138a8f1f8 (MD5) Previous issue date: 2016-07-08","Embargo set by: Seth Robbins for item 95348 Lift date: 2018-11-10T18:28:02Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 95348 on 2018-11-11T10:15:28Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables"]}]}],"canonical_facts":{"dc:contributor":["Chen, Yuguo","Culpepper, Steven A.","Marden, John I.","Simpson, Douglas G."],"dc:creator":["Eisinger, Robert David"],"dc:date":["2016-11-10T18:27:42Z","2018-11-11T10:15:28Z","2016-07-08","2016-08"],"dc:description":["We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals for the method are constructed by leveraging approximations to the total number of structures (tables, multigraphs, or high-dimensional tables), based on results in the literature. The methods generate structures that are very close to the target uniform distribution. Along with their importance weights, the data structures are used to approximate the null distribution of test statistics. In the case of contingency tables, we apply the methods to a number of applications and demonstrate an improvement over competing methods. For loopless, undirected multigraphs, we apply the method to ecological and security problems, and demonstrate excellent performance. In the case of high-dimensional tables, we apply the sequential importance sampling method to the analysis of multimarker linkage disequilibrium data and also demonstrate excellent performance.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-08-01","The student, Robert Eisinger, accepted the attached license on 2016-07-07 at 10:35.","The student, Robert Eisinger, submitted this Dissertation for approval on 2016-07-07 at 10:48.","This Dissertation was approved for publication on 2016-07-08 at 09:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9792 on 2016-11-10 at 12:20:21","Made available in DSpace on 2016-11-10T18:27:42Z (GMT). No. of bitstreams: 2 EISINGER-DISSERTATION-2016.pdf: 1041551 bytes, checksum: 99a90bbf6d8dde71d46b47903505008b (MD5) LICENSE.txt: 4212 bytes, checksum: c5da0c98593853addf8d27f138a8f1f8 (MD5) Previous issue date: 2016-07-08","Embargo set by: Seth Robbins for item 95348 Lift date: 2018-11-10T18:28:02Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 95348 on 2018-11-11T10:15:28Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/92928"],"dc:language":["en"],"dc:rights":["Copyright 2016 Robert Eisinger"],"dc:subject":["Monte Carlo method","Sequential importance sampling","Counting problem","Contingency Table"],"dc:title":["Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables"],"dc:type":["text"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:35Z"}