{"id":"https://openalex.org/W2145117186","doi":"https://doi.org/10.1109/glocom.2005.1578076","title":"A Bayesian estimator for correlation parameters of the multivariate nakagami-m distribution","display_name":"A Bayesian estimator for correlation parameters of the multivariate nakagami-m distribution","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W2145117186","doi":"https://doi.org/10.1109/glocom.2005.1578076","mag":"2145117186"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2005.1578076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2005.1578076","pdf_url":null,"source":{"id":"https://openalex.org/S4363608563","display_name":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065922187","display_name":"Zhou Mingxin","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingxin Zhou","raw_affiliation_strings":["Department EE, Tsinghua University, Beijing, China","Dept.of Electr. Eng., Tsinghua Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"Department EE, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept.of Electr. Eng., Tsinghua Univ., Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100396834","display_name":"Hao Zhang","orcid":"https://orcid.org/0000-0001-9786-5008"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Zhang","raw_affiliation_strings":["Department EE, Tsinghua University, Beijing, China","Dept.of Electr. Eng., Tsinghua Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"Department EE, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept.of Electr. Eng., Tsinghua Univ., Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018270522","display_name":"Yingning Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingning Peng","raw_affiliation_strings":["Department EE, Tsinghua University, Beijing, China","Dept.of Electr. Eng., Tsinghua Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"Department EE, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept.of Electr. Eng., Tsinghua Univ., Beijing","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065922187"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28697572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2312","last_page":"2316"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7268022298812866},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.603082001209259},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6025303602218628},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5989505648612976},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5645875930786133},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.4864358901977539},{"id":"https://openalex.org/keywords/metropolis\u2013hastings-algorithm","display_name":"Metropolis\u2013Hastings algorithm","score":0.47289788722991943},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4663991332054138},{"id":"https://openalex.org/keywords/bayes-estimator","display_name":"Bayes estimator","score":0.4648500978946686},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.4604303240776062},{"id":"https://openalex.org/keywords/nakagami-distribution","display_name":"Nakagami distribution","score":0.44494277238845825},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4290272891521454},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.38961660861968994},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35096800327301025},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.22506961226463318},{"id":"https://openalex.org/keywords/fading","display_name":"Fading","score":0.09269604086875916}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7268022298812866},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.603082001209259},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6025303602218628},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5989505648612976},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5645875930786133},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.4864358901977539},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.47289788722991943},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4663991332054138},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.4648500978946686},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.4604303240776062},{"id":"https://openalex.org/C115098869","wikidata":"https://www.wikidata.org/wiki/Q3258347","display_name":"Nakagami distribution","level":4,"score":0.44494277238845825},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4290272891521454},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.38961660861968994},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35096800327301025},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.22506961226463318},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.09269604086875916},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/glocom.2005.1578076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2005.1578076","pdf_url":null,"source":{"id":"https://openalex.org/S4363608563","display_name":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1576718945","https://openalex.org/W1985093013","https://openalex.org/W2106809303","https://openalex.org/W2108740391","https://openalex.org/W2126304552","https://openalex.org/W2155023848","https://openalex.org/W4292403327"],"related_works":["https://openalex.org/W1607416738","https://openalex.org/W2524789634","https://openalex.org/W2071668645","https://openalex.org/W2108207895","https://openalex.org/W2216956849","https://openalex.org/W4250051631","https://openalex.org/W1557932906","https://openalex.org/W2023579776","https://openalex.org/W2922302762","https://openalex.org/W2135908887"],"abstract_inverted_index":{"We":[0],"presented":[1],"a":[2,19,23,44,55,117],"novel":[3],"Bayesian":[4],"estimator":[5,17,107],"for":[6],"the":[7,11,28,35,39,50,62,66,75,86,92,97],"correlation":[8],"parameters":[9],"of":[10,38,78],"multivariate":[12],"Nakagami-m":[13],"distributions.":[14],"The":[15],"proposed":[16],"has":[18],"two-stage":[20],"structure.":[21],"First,":[22],"generalized":[24],"Gibbs":[25,52],"sampler":[26,30],"-":[27],"group-based":[29,63],"is":[31,59,108],"constructed":[32],"to":[33,43,49,91],"analyze":[34],"posterior":[36],"distributions":[37],"parameters,":[40],"which":[41],"leads":[42],"lower":[45,89],"time":[46],"complexity":[47],"compared":[48,90],"traditional":[51,93],"sampler.":[53,64],"Second,":[54],"multi-proposal":[56],"Metropolis-Hastings":[57,67],"algorithm":[58],"implemented":[60],"within":[61],"In":[65],"stage,":[68],"estimation":[69,87,120],"values":[70],"are":[71],"obtained":[72],"by":[73],"computing":[74],"weighted":[76],"mean":[77],"both":[79],"accepted":[80,98],"and":[81,114],"rejected":[82],"states.":[83,99],"It":[84],"makes":[85],"variance":[88],"methods":[94],"using":[95],"only":[96],"Numerical":[100],"simulation":[101],"results":[102],"demonstrate":[103],"that":[104],"our":[105],"new":[106],"practically":[109],"unbiased":[110],"except":[111],"some":[112],"points":[113],"it":[115],"offers":[116],"relatively":[118],"small":[119],"variance.":[121]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
