{"id":"https://openalex.org/W3133707149","doi":"https://doi.org/10.3390/e23030318","title":"Bayesian Analysis of Finite Populations under Simple Random Sampling","display_name":"Bayesian Analysis of Finite Populations under Simple Random Sampling","publication_year":2021,"publication_date":"2021-03-08","ids":{"openalex":"https://openalex.org/W3133707149","doi":"https://doi.org/10.3390/e23030318","mag":"3133707149","pmid":"https://pubmed.ncbi.nlm.nih.gov/33800337"},"language":"en","primary_location":{"id":"doi:10.3390/e23030318","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23030318","pdf_url":"https://www.mdpi.com/1099-4300/23/3/318/pdf?version=1615877233","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/23/3/318/pdf?version=1615877233","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066304299","display_name":"Manuel E. Mendoza","orcid":"https://orcid.org/0000-0003-1310-9702"},"institutions":[{"id":"https://openalex.org/I187481652","display_name":"Instituto Tecnol\u00f3gico Aut\u00f3nomo de M\u00e9xico","ror":"https://ror.org/029md1766","country_code":"MX","type":"education","lineage":["https://openalex.org/I187481652"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Manuel Mendoza","raw_affiliation_strings":["Departamento de Estad\u00edstica, Instituto Tecnol\u00f3gico Aut\u00f3nomo de M\u00e9xico, R\u00edo Hondo 1, Ciudad de M\u00e9xico 01080, Mexico"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departamento de Estad\u00edstica, Instituto Tecnol\u00f3gico Aut\u00f3nomo de M\u00e9xico, R\u00edo Hondo 1, Ciudad de M\u00e9xico 01080, Mexico","institution_ids":["https://openalex.org/I187481652"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101653345","display_name":"Alberto Contreras\u2010Crist\u00e1n","orcid":"https://orcid.org/0000-0002-3445-8015"},"institutions":[{"id":"https://openalex.org/I8961855","display_name":"Universidad Nacional Aut\u00f3noma de M\u00e9xico","ror":"https://ror.org/01tmp8f25","country_code":"MX","type":"education","lineage":["https://openalex.org/I8961855"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Alberto Contreras-Crist\u00e1n","raw_affiliation_strings":["Departamento de Probabilidad y Estad\u00edstica, Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Apartado Postal 20-126, Ciudad de M\u00e9xico 01000, Mexico"],"raw_orcid":"https://orcid.org/0000-0002-3445-8015","affiliations":[{"raw_affiliation_string":"Departamento de Probabilidad y Estad\u00edstica, Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Apartado Postal 20-126, Ciudad de M\u00e9xico 01000, Mexico","institution_ids":["https://openalex.org/I8961855"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088560515","display_name":"Eduardo Guti\u00e9rrez\u2010Pe\u00f1a","orcid":"https://orcid.org/0000-0003-4706-5226"},"institutions":[{"id":"https://openalex.org/I8961855","display_name":"Universidad Nacional Aut\u00f3noma de M\u00e9xico","ror":"https://ror.org/01tmp8f25","country_code":"MX","type":"education","lineage":["https://openalex.org/I8961855"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Eduardo Guti\u00e9rrez-Pe\u00f1a","raw_affiliation_strings":["Departamento de Probabilidad y Estad\u00edstica, Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Apartado Postal 20-126, Ciudad de M\u00e9xico 01000, Mexico"],"raw_orcid":"https://orcid.org/0000-0003-4706-5226","affiliations":[{"raw_affiliation_string":"Departamento de Probabilidad y Estad\u00edstica, Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Apartado Postal 20-126, Ciudad de M\u00e9xico 01000, Mexico","institution_ids":["https://openalex.org/I8961855"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066304299"],"corresponding_institution_ids":["https://openalex.org/I187481652"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.9794,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80027407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"23","issue":"3","first_page":"318","last_page":"318"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9573000073432922,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9388999938964844,"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/simple-random-sample","display_name":"Simple random sample","score":0.6623608469963074},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.6263855695724487},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6248530149459839},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5917672514915466},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5678130388259888},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5539469122886658},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5345636606216431},{"id":"https://openalex.org/keywords/sampling-design","display_name":"Sampling design","score":0.505549430847168},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4868699610233307},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.48664912581443787},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.47915881872177124},{"id":"https://openalex.org/keywords/survey-sampling","display_name":"Survey sampling","score":0.4766572117805481},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4475564956665039},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4375460743904114},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3676193952560425},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.2873297929763794}],"concepts":[{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.6623608469963074},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.6263855695724487},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6248530149459839},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5917672514915466},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5678130388259888},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5539469122886658},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5345636606216431},{"id":"https://openalex.org/C75373757","wikidata":"https://www.wikidata.org/wiki/Q7410160","display_name":"Sampling design","level":3,"score":0.505549430847168},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4868699610233307},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.48664912581443787},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.47915881872177124},{"id":"https://openalex.org/C5733905","wikidata":"https://www.wikidata.org/wiki/Q10744315","display_name":"Survey sampling","level":3,"score":0.4766572117805481},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4475564956665039},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4375460743904114},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3676193952560425},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2873297929763794},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e23030318","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23030318","pdf_url":"https://www.mdpi.com/1099-4300/23/3/318/pdf?version=1615877233","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:33800337","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33800337","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:04316a81f1214fb89e0dad7257912c8f","is_oa":true,"landing_page_url":"https://doaj.org/article/04316a81f1214fb89e0dad7257912c8f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 23, Iss 3, p 318 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/23/3/318/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e23030318","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy; Volume 23; Issue 3; Pages: 318","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7998389","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7998389","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e23030318","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23030318","pdf_url":"https://www.mdpi.com/1099-4300/23/3/318/pdf?version=1615877233","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4000000059604645,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3133707149.pdf"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W621295166","https://openalex.org/W1548584376","https://openalex.org/W1974844579","https://openalex.org/W1977404151","https://openalex.org/W1980075455","https://openalex.org/W1980655035","https://openalex.org/W2017436910","https://openalex.org/W2061828456","https://openalex.org/W2062207349","https://openalex.org/W2063390378","https://openalex.org/W2066583279","https://openalex.org/W2069429561","https://openalex.org/W2082583927","https://openalex.org/W2091797506","https://openalex.org/W2118942461","https://openalex.org/W2126232165","https://openalex.org/W2151967501","https://openalex.org/W2296879939","https://openalex.org/W2408353371","https://openalex.org/W2526112356","https://openalex.org/W2530327133","https://openalex.org/W2738876637","https://openalex.org/W2797333853","https://openalex.org/W2905452241","https://openalex.org/W2963140874","https://openalex.org/W3015849080","https://openalex.org/W3102720765","https://openalex.org/W3141232563","https://openalex.org/W4254913927","https://openalex.org/W6606970410","https://openalex.org/W6679011611","https://openalex.org/W6682569104","https://openalex.org/W6741447382","https://openalex.org/W6785749335"],"related_works":["https://openalex.org/W4238714840","https://openalex.org/W2350399852","https://openalex.org/W336480102","https://openalex.org/W2555206834","https://openalex.org/W225930446","https://openalex.org/W2091497721","https://openalex.org/W4388650891","https://openalex.org/W2034762654","https://openalex.org/W2362618132","https://openalex.org/W2339877733"],"abstract_inverted_index":{"have":[0],"become":[1],"part":[2,22],"of":[3,6,13,23,46,73,92,104,125,155],"the":[4,7,24,28,32,47,81,89,102,128,153],"mainstream":[5],"statistical":[8,25,48],"methodology.":[9],"A":[10],"wide":[11],"variety":[12],"sampling":[14,84],"schemes":[15],"as":[16,18,70,131,152],"well":[17],"estimators":[19],"are":[20],"now":[21,36],"folklore.":[26],"On":[27],"other":[29],"hand,":[30],"while":[31],"Bayesian":[33,76,90],"approach":[34,111],"is":[35,97,106,112],"a":[37,56,71,109],"well-established":[38],"paradigm":[39],"with":[40,61,101],"implications":[41],"in":[42,80,127,148,158],"almost":[43],"every":[44],"field":[45],"arena,":[49],"there":[50],"does":[51],"not":[52],"seem":[53],"to":[54,59],"exist":[55],"conventional":[57],"procedure-able":[58],"deal":[60],"both":[62],"continuous":[63],"and":[64,108,122,135],"discrete":[65],"variables-that":[66],"can":[67,116,139],"be":[68],"used":[69],"kind":[72],"default":[74],"for":[75,119,132],"survey":[77],"sampling,":[78],"even":[79],"simple":[82],"random":[83],"case.":[85],"In":[86],"this":[87],"paper,":[88],"analysis":[91,154],"samples":[93],"from":[94],"finite":[95],"populations":[96],"discussed,":[98],"its":[99],"relationship":[100],"notion":[103],"superpopulation":[105],"reviewed,":[107],"nonparametric":[110],"proposed.":[113],"Our":[114],"proposal":[115],"produce":[117],"inferences":[118],"population":[120,133],"quantiles":[121],"similar":[123],"quantities":[124],"interest":[126],"same":[129],"way":[130],"means":[134],"totals.":[136],"Moreover,":[137],"it":[138],"provide":[140],"results":[141],"relatively":[142],"quickly,":[143],"which":[144],"may":[145],"prove":[146],"crucial":[147],"certain":[149],"contexts":[150],"such":[151],"quick":[156],"counts":[157],"electoral":[159],"settings.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2025-10-10T00:00:00"}