{"id":"https://openalex.org/W3215989767","doi":"https://doi.org/10.1080/03610918.2022.2050396","title":"Trimmed Harrell-Davis quantile estimator based on the highest density interval of the given width","display_name":"Trimmed Harrell-Davis quantile estimator based on the highest density interval of the given width","publication_year":2022,"publication_date":"2022-03-17","ids":{"openalex":"https://openalex.org/W3215989767","doi":"https://doi.org/10.1080/03610918.2022.2050396","mag":"3215989767"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2022.2050396","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2050396","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2111.11776","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036650421","display_name":"Andrey Akinshin","orcid":"https://orcid.org/0000-0003-3553-9367"},"institutions":[{"id":"https://openalex.org/I186776151","display_name":"Rider University","ror":"https://ror.org/01dgn5344","country_code":"US","type":"education","lineage":["https://openalex.org/I186776151"]},{"id":"https://openalex.org/I4210108022","display_name":"Jet Company (Czechia)","ror":"https://ror.org/011drtn64","country_code":"CZ","type":"company","lineage":["https://openalex.org/I4210108022"]}],"countries":["CZ","US"],"is_corresponding":true,"raw_author_name":"Andrey Akinshin","raw_affiliation_strings":["JetBrains sro, Rider","JetBrains sro, Rider, Praha, Czech Republic"],"affiliations":[{"raw_affiliation_string":"JetBrains sro, Rider","institution_ids":["https://openalex.org/I186776151"]},{"raw_affiliation_string":"JetBrains sro, Rider, Praha, Czech Republic","institution_ids":["https://openalex.org/I4210108022"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5036650421"],"corresponding_institution_ids":["https://openalex.org/I186776151","https://openalex.org/I4210108022"],"apc_list":null,"apc_paid":null,"fwci":2.2177,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.88173401,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"53","issue":"3","first_page":"1565","last_page":"1575"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9958000183105469,"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":0.9958000183105469,"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":0.9950000047683716,"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/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.9214690327644348},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.8732008934020996},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.711689829826355},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.661304771900177},{"id":"https://openalex.org/keywords/order-statistic","display_name":"Order statistic","score":0.644507884979248},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5403398275375366},{"id":"https://openalex.org/keywords/trimmed-estimator","display_name":"Trimmed estimator","score":0.4781035780906677},{"id":"https://openalex.org/keywords/efficiency","display_name":"Efficiency","score":0.46842634677886963},{"id":"https://openalex.org/keywords/truncated-mean","display_name":"Truncated mean","score":0.45224863290786743},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.44560468196868896},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3365969657897949},{"id":"https://openalex.org/keywords/efficient-estimator","display_name":"Efficient estimator","score":0.2776196599006653},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.21626749634742737}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.9214690327644348},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.8732008934020996},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.711689829826355},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.661304771900177},{"id":"https://openalex.org/C44082924","wikidata":"https://www.wikidata.org/wiki/Q1767128","display_name":"Order statistic","level":2,"score":0.644507884979248},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5403398275375366},{"id":"https://openalex.org/C41020250","wikidata":"https://www.wikidata.org/wiki/Q17144114","display_name":"Trimmed estimator","level":5,"score":0.4781035780906677},{"id":"https://openalex.org/C17648541","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficiency","level":3,"score":0.46842634677886963},{"id":"https://openalex.org/C116714509","wikidata":"https://www.wikidata.org/wiki/Q32278","display_name":"Truncated mean","level":3,"score":0.45224863290786743},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.44560468196868896},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3365969657897949},{"id":"https://openalex.org/C35594927","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficient estimator","level":4,"score":0.2776196599006653},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.21626749634742737},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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":2,"locations":[{"id":"doi:10.1080/03610918.2022.2050396","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2050396","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"},{"id":"pmh:oai:arXiv.org:2111.11776","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.11776","pdf_url":"https://arxiv.org/pdf/2111.11776","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2111.11776","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.11776","pdf_url":"https://arxiv.org/pdf/2111.11776","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W578879968","https://openalex.org/W1495690663","https://openalex.org/W1970375475","https://openalex.org/W1985710418","https://openalex.org/W2033046771","https://openalex.org/W2053619222","https://openalex.org/W2079042375","https://openalex.org/W2135874888","https://openalex.org/W2155063211","https://openalex.org/W2476917595","https://openalex.org/W2504767107","https://openalex.org/W2963762572","https://openalex.org/W2999188005","https://openalex.org/W4245289321","https://openalex.org/W4247996174","https://openalex.org/W4285579170","https://openalex.org/W4300940930","https://openalex.org/W4399607918","https://openalex.org/W6888037560"],"related_works":["https://openalex.org/W618533035","https://openalex.org/W2780369879","https://openalex.org/W805672513","https://openalex.org/W2076875295","https://openalex.org/W2026281612","https://openalex.org/W1984144750","https://openalex.org/W3145452682","https://openalex.org/W2090574447","https://openalex.org/W2067817134","https://openalex.org/W1505403183"],"abstract_inverted_index":{"Traditional":[0],"quantile":[1,45,91],"estimators":[2,26],"that":[3],"are":[4,12,27],"based":[5,20],"on":[6,21],"one":[7],"or":[8],"two":[9],"order":[10,54,98],"statistics":[11,99],"a":[13,49,85],"common":[14],"way":[15],"to":[16,73,104],"estimate":[17],"distribution":[18],"quantiles":[19],"the":[22,43,64,75,89,105,110],"given":[23],"samples.":[24],"These":[25],"robust,":[28],"but":[29],"their":[30],"statistical":[31,78],"efficiency":[32,79],"is":[33,42],"not":[34,68],"always":[35],"good":[36],"enough.":[37],"A":[38],"more":[39,60],"efficient":[40],"alternative":[41],"Harrell-Davis":[44,90],"estimator":[46],"which":[47],"uses":[48],"weighted":[50],"sum":[51],"of":[52,88,109],"all":[53],"statistics.":[55],"Whereas":[56],"this":[57,94],"approach":[58],"provides":[59],"accurate":[61],"estimations":[62],"for":[63],"light-tailed":[65],"distributions,":[66],"it\u2019s":[67],"robust.":[69],"To":[70],"be":[71],"able":[72],"customize":[74],"tradeoff":[76],"between":[77],"and":[80],"robustness,":[81],"we":[82,96],"could":[83],"consider":[84],"trimmed":[86],"modification":[87],"estimator.":[92],"In":[93],"approach,":[95],"discard":[97],"with":[100],"low":[101],"weights":[102],"according":[103],"highest":[106],"density":[107],"interval":[108],"beta":[111],"distribution.":[112]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
