{"id":"https://openalex.org/W1975214616","doi":"https://doi.org/10.5691/jjb.20.143","title":"Bias-Corrected Estimator and Confidence Intervals Based on the Monte Carlo Method","display_name":"Bias-Corrected Estimator and Confidence Intervals Based on the Monte Carlo Method","publication_year":2000,"publication_date":"2000-01-01","ids":{"openalex":"https://openalex.org/W1975214616","doi":"https://doi.org/10.5691/jjb.20.143","mag":"1975214616"},"language":"en","primary_location":{"id":"doi:10.5691/jjb.20.143","doi":"https://doi.org/10.5691/jjb.20.143","is_oa":true,"landing_page_url":"https://doi.org/10.5691/jjb.20.143","pdf_url":"https://www.jstage.jst.go.jp/article/jjb/20/2/20_2_143/_pdf","source":{"id":"https://openalex.org/S4210233217","display_name":"Japanese Journal of Biometrics","issn_l":"0918-4430","issn":["0918-4430","2185-6494"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Japanese Journal of Biometrics","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.jstage.jst.go.jp/article/jjb/20/2/20_2_143/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065722901","display_name":"Hiroshi Hakoyama","orcid":"https://orcid.org/0000-0001-7464-0754","observed_orcids":["https://orcid.org/0000-0001-7464-0754"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hiroshi Hakoyama","raw_affiliation_strings":["Hokkaido National Fisheries Research Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hokkaido National Fisheries Research Institute","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031916507","display_name":"Yoh Iwasa","orcid":"https://orcid.org/0000-0003-0691-1852","observed_orcids":["https://orcid.org/0000-0003-0691-1852"]},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]},{"id":"https://openalex.org/I4210086780","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19","country_code":"JP","type":"government","lineage":["https://openalex.org/I1319490839","https://openalex.org/I4210086780"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoh Iwasa","raw_affiliation_strings":["Department of Biology, Kyushu University, CREST, Japan Science and Technology Corporation (JST)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biology, Kyushu University, CREST, Japan Science and Technology Corporation (JST)","institution_ids":["https://openalex.org/I135598925","https://openalex.org/I4210086780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2163,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.79299691,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":"2","first_page":"143","last_page":"154"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.7445999979972839,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.7445999979972839,"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/T10482","display_name":"Mathematical and Theoretical Epidemiology and Ecology Models","score":0.0722000002861023,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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":0.0551999993622303,"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/bias-correction","display_name":"bias correction","score":0.9169999957084656},{"id":"https://openalex.org/keywords/bias-corrected-estimators","display_name":"bias-corrected estimators","score":0.8799999952316284},{"id":"https://openalex.org/keywords/confidence-intervals","display_name":"confidence intervals","score":0.8960000276565552},{"id":"https://openalex.org/keywords/monte-carlo-sampling","display_name":"Monte Carlo sampling","score":0.8190000057220459},{"id":"https://openalex.org/keywords/small-sample-bias","display_name":"small sample bias","score":0.703000009059906},{"id":"https://openalex.org/keywords/approximate-maximum-likelihood-estimation","display_name":"approximate maximum likelihood estimation","score":0.8550000190734863},{"id":"https://openalex.org/keywords/stochastic-differential-equations","display_name":"stochastic differential equations","score":0.7329999804496765},{"id":"https://openalex.org/keywords/logistic-growth-model","display_name":"logistic growth model","score":0.777999997138977},{"id":"https://openalex.org/keywords/environmental-stochasticity","display_name":"environmental stochasticity","score":0.824999988079071},{"id":"https://openalex.org/keywords/demographic-stochasticity","display_name":"demographic stochasticity","score":0.8700000047683716}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7610540986061096},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.7185534834861755},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.713040292263031},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6433433294296265},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.6336489319801331},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.5560805797576904},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.5541478991508484},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.46693629026412964},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4562950134277344},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.4328545331954956},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.42313697934150696},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.14900290966033936},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.14302152395248413},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5691/jjb.20.143","doi":"https://doi.org/10.5691/jjb.20.143","is_oa":true,"landing_page_url":"https://doi.org/10.5691/jjb.20.143","pdf_url":"https://www.jstage.jst.go.jp/article/jjb/20/2/20_2_143/_pdf","source":{"id":"https://openalex.org/S4210233217","display_name":"Japanese Journal of Biometrics","issn_l":"0918-4430","issn":["0918-4430","2185-6494"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Japanese Journal of Biometrics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.5691/jjb.20.143","doi":"https://doi.org/10.5691/jjb.20.143","is_oa":true,"landing_page_url":"https://doi.org/10.5691/jjb.20.143","pdf_url":"https://www.jstage.jst.go.jp/article/jjb/20/2/20_2_143/_pdf","source":{"id":"https://openalex.org/S4210233217","display_name":"Japanese Journal of Biometrics","issn_l":"0918-4430","issn":["0918-4430","2185-6494"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Japanese Journal of Biometrics","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1975214616.pdf","grobid_xml":"https://content.openalex.org/works/W1975214616.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W1995945562","https://openalex.org/W2047831247","https://openalex.org/W2051856569","https://openalex.org/W2100577882","https://openalex.org/W3106889297"],"related_works":["https://openalex.org/W2036715754","https://openalex.org/W2185272062","https://openalex.org/W4234234693","https://openalex.org/W2078386095","https://openalex.org/W2621318916","https://openalex.org/W1521247714","https://openalex.org/W4251332806","https://openalex.org/W2005766757","https://openalex.org/W2328123077","https://openalex.org/W2040370679"],"abstract_inverted_index":{"To":[0,178],"estimate":[1,146,209],"parameters":[2,212],"without":[3],"bias":[4,27,44,144,244],"and":[5,48,75,148,154,173,201,220],"to":[6,62,188],"determine":[7],"confidence":[8,50,150],"intervals":[9,51,151],"of":[10,41,92,182,210,234],"the":[11,19,29,54,69,89,93,104,127,137,143,157,168,180,230,243,248],"parameter":[12,46,107],"in":[13,45],"general":[14],"cases":[15],"are":[16,175],"difficult.":[17],"Even":[18],"maximum":[20,206],"likelihood":[21,207],"(ML)":[22],"estimators":[23],"may":[24],"have":[25],"significant":[26,226],"if":[28,171,229],"sample-size":[30],"is":[31,60,101,136,167,237],"small.":[32],"In":[33],"this":[34,183],"paper,":[35],"we":[36,119,185,240],"develop":[37],"a":[38,77,110,114,189,195,225],"simple":[39],"method":[40,59],"removing":[42],"large":[43,115],"estimation":[47],"calculating":[49],"based":[52],"on":[53],"Monte":[55,158,249],"Carlo":[56,159,250],"sampling.":[57,251],"The":[58],"applicable":[61],"any":[63],"parameteric":[64],"models":[65],"for":[66,97,194],"which":[67,125],"(1)":[68],"computer":[70],"simulation":[71],"can":[72,83,120,141,163,241],"be":[73,84,88],"performed":[74],"(2)":[76],"biased":[78],"(but":[79],"correctable)":[80],"estimator":[81,94,112,170],"*bias":[82,174],"constructed.":[85],"Let":[86],"E[*|\u03b8]":[87],"expected":[90],"value":[91],"*(s*)":[95],"calculated":[96],"data":[98,233],"s*":[99],"that":[100,165],"generated":[102],"by":[103,152,247],"model":[105,193],"with":[106,113,199],"\u03b8.":[108],"For":[109],"given":[111],"bias,":[116,227],"*":[117],"bias\u2019":[118],"calculate":[121],"bias-corrected":[122],"estimator,":[123],"*bc\u2019":[124],"satisfies":[126],"following":[128],"relationship;":[129],"E[*bias(s*)|*bc":[130],"(s)]":[131],"=":[132],"*bias(s),":[133],"where":[134],"s":[135],"observed":[138],"data.":[139],"We":[140,162],"find":[142],"corrected":[145],"*bc(s)":[147],"its":[149],"trial":[153],"error,":[155],"using":[156],"sampling":[160],"repeatedly.":[161],"prove":[164],"*bc":[166],"unbiased":[169],"\u03b8":[172],"linearly":[176],"related.":[177],"illustrate":[179],"use":[181],"method,":[184],"apply":[186],"it":[187],"stochastic":[190],"differential":[191],"equation":[192],"logistically":[196],"growing":[197],"population":[198,235],"environmental":[200,221],"demographic":[202],"stochasticities.":[203],"An":[204],"approximate":[205],"(AML)":[208],"three":[211],"(intrinsic":[213],"growth":[214],"rate":[215],"r,":[216],"carrying":[217],"capacity":[218],"K,":[219],"stochasticity":[222],"**)":[223],"has":[224],"especially":[228],"time":[231],"series":[232],"size":[236],"short.":[238],"However":[239],"remove":[242],"very":[245],"effectively":[246]},"counts_by_year":[],"updated_date":"2026-08-10T07:06:39.071514","created_date":"2016-06-24T00:00:00"}
