{"id":"https://openalex.org/W2973081716","doi":"https://doi.org/10.1080/03610918.2019.1662043","title":"Representativeness of ranked set sampling based on Bayesian score","display_name":"Representativeness of ranked set sampling based on Bayesian score","publication_year":2019,"publication_date":"2019-09-11","ids":{"openalex":"https://openalex.org/W2973081716","doi":"https://doi.org/10.1080/03610918.2019.1662043","mag":"2973081716"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2019.1662043","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1662043","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-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/A5029252430","display_name":"Vivek Verma","orcid":null},"institutions":[{"id":"https://openalex.org/I138537684","display_name":"Gauhati University","ror":"https://ror.org/01ppj9r51","country_code":"IN","type":"education","lineage":["https://openalex.org/I138537684"]},{"id":"https://openalex.org/I63739035","display_name":"All India Institute of Medical Sciences","ror":"https://ror.org/02dwcqs71","country_code":"IN","type":"education","lineage":["https://openalex.org/I2799351866","https://openalex.org/I4210148677","https://openalex.org/I63739035"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vivek Verma","raw_affiliation_strings":["Department of Neurology, All India Institute of Medical Sciences, New Delhi, India","Department of Statistics, Gauhati University, Guwahati, India"],"affiliations":[{"raw_affiliation_string":"Department of Neurology, All India Institute of Medical Sciences, New Delhi, India","institution_ids":["https://openalex.org/I63739035"]},{"raw_affiliation_string":"Department of Statistics, Gauhati University, Guwahati, India","institution_ids":["https://openalex.org/I138537684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073248510","display_name":"Radhakanta Das","orcid":"https://orcid.org/0000-0001-6149-8947"},"institutions":[{"id":"https://openalex.org/I157674215","display_name":"Presidency University","ror":"https://ror.org/04xgbph11","country_code":"IN","type":"education","lineage":["https://openalex.org/I157674215"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Radhakanta Das","raw_affiliation_strings":["Department of Statistics, Presidency University, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Presidency University, Kolkata, India","institution_ids":["https://openalex.org/I157674215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112541219","display_name":"Dilip C. Nath","orcid":null},"institutions":[{"id":"https://openalex.org/I49278261","display_name":"Assam University","ror":"https://ror.org/0535c1v66","country_code":"IN","type":"education","lineage":["https://openalex.org/I49278261"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Dilip C. Nath","raw_affiliation_strings":["Assam University, Silchar, India"],"affiliations":[{"raw_affiliation_string":"Assam University, Silchar, India","institution_ids":["https://openalex.org/I49278261"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112541219"],"corresponding_institution_ids":["https://openalex.org/I49278261"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12840031,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"51","issue":"3","first_page":"1080","last_page":"1095"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9995999932289124,"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/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.98089998960495,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9758999943733215,"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/representativeness-heuristic","display_name":"Representativeness heuristic","score":0.9383080005645752},{"id":"https://openalex.org/keywords/rss","display_name":"RSS","score":0.9176219701766968},{"id":"https://openalex.org/keywords/simple-random-sample","display_name":"Simple random sample","score":0.6934175491333008},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6854033470153809},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6200476884841919},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5803927183151245},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5360003113746643},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5306645035743713},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4869133234024048},{"id":"https://openalex.org/keywords/sampling-design","display_name":"Sampling design","score":0.47618308663368225},{"id":"https://openalex.org/keywords/poisson-sampling","display_name":"Poisson sampling","score":0.47581297159194946},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.448202908039093},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.42325663566589355},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41160136461257935},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3978143632411957},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2828240692615509},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16640114784240723},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.1380767822265625},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09754619002342224},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.06277966499328613}],"concepts":[{"id":"https://openalex.org/C37381756","wikidata":"https://www.wikidata.org/wiki/Q20203288","display_name":"Representativeness heuristic","level":2,"score":0.9383080005645752},{"id":"https://openalex.org/C2385561","wikidata":"https://www.wikidata.org/wiki/Q45432","display_name":"RSS","level":2,"score":0.9176219701766968},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.6934175491333008},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6854033470153809},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6200476884841919},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5803927183151245},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5360003113746643},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5306645035743713},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4869133234024048},{"id":"https://openalex.org/C75373757","wikidata":"https://www.wikidata.org/wiki/Q7410160","display_name":"Sampling design","level":3,"score":0.47618308663368225},{"id":"https://openalex.org/C82152865","wikidata":"https://www.wikidata.org/wiki/Q7208505","display_name":"Poisson sampling","level":5,"score":0.47581297159194946},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.448202908039093},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.42325663566589355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41160136461257935},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3978143632411957},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2828240692615509},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16640114784240723},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.1380767822265625},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09754619002342224},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.06277966499328613},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2019.1662043","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1662043","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320719","display_name":"Department of Science and Technology, Ministry of Science and Technology, India","ror":"https://ror.org/0101xrq71"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W588750982","https://openalex.org/W1986049907","https://openalex.org/W1988693679","https://openalex.org/W1993141720","https://openalex.org/W1997955518","https://openalex.org/W2004279191","https://openalex.org/W2008408176","https://openalex.org/W2017089182","https://openalex.org/W2022093226","https://openalex.org/W2027796863","https://openalex.org/W2028324842","https://openalex.org/W2032216509","https://openalex.org/W2039847534","https://openalex.org/W2080774619","https://openalex.org/W2081427904","https://openalex.org/W2101049917","https://openalex.org/W2138014857","https://openalex.org/W2146876168","https://openalex.org/W2156404878","https://openalex.org/W2301291474","https://openalex.org/W2316869977","https://openalex.org/W2520207369","https://openalex.org/W2604272474","https://openalex.org/W2766896451","https://openalex.org/W2791009414","https://openalex.org/W2793389993","https://openalex.org/W4245274258","https://openalex.org/W4246999471"],"related_works":["https://openalex.org/W336480102","https://openalex.org/W2970497550","https://openalex.org/W4238714840","https://openalex.org/W4235701227","https://openalex.org/W3047864323","https://openalex.org/W1975123916","https://openalex.org/W2255225906","https://openalex.org/W4225149851","https://openalex.org/W4240967125","https://openalex.org/W1492735889"],"abstract_inverted_index":{"The":[0,52,93],"present":[1],"article":[2],"is":[3,33,58,72,89,96],"concerned":[4],"with":[5,27],"the":[6,15,37,47,50,64,68,87],"comparison":[7],"of":[8,10,43,49,66,77,82,85,105,116],"representativeness":[9,44],"a":[11,19,41,59,100],"random":[12,29],"sample":[13,71],"to":[14,46],"underlying":[16],"population":[17],"as":[18,40],"whole,":[20],"obtained":[21],"through":[22,99],"ranked":[23],"set":[24],"sampling":[25,30,61],"(RSS)":[26],"simple":[28],"(SRS).":[31],"This":[32],"done":[34],"by":[35],"defining":[36],"Bayesian":[38],"score":[39],"measure":[42],"corresponding":[45],"units":[48],"population.":[51],"proposed":[53,94],"procedure":[54,95],"shows":[55],"that":[56],"RSS":[57,78],"better":[60],"method":[62],"and":[63],"probability":[65],"having":[67],"most":[69],"unrepresentative":[70],"very":[73],"less":[74],"in":[75,80,110],"case":[76,81],"than":[79],"SRS,":[83],"irrespective":[84],"whether":[86],"ranking":[88],"perfect":[90],"or":[91],"imperfect.":[92],"also":[97],"illustrated":[98],"real-life":[101],"data":[102],"on":[103],"survivorship":[104],"children":[106],"below":[107],"one":[108],"year":[109],"Empowered":[111],"Action":[112],"Groups":[113],"(EAG)":[114],"states":[115],"India.":[117]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
