{"id":"https://openalex.org/W2911804094","doi":"https://doi.org/10.3103/s0146411618060081","title":"A New Technique for Intelligent Constructing Exact \u03b3-content Tolerance Limits with Expected (1 \u2013 \u03b1)-confidence on Future Outcomes in the Weibull Case Using Complete or Type II Censored Data","display_name":"A New Technique for Intelligent Constructing Exact \u03b3-content Tolerance Limits with Expected (1 \u2013 \u03b1)-confidence on Future Outcomes in the Weibull Case Using Complete or Type II Censored Data","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2911804094","doi":"https://doi.org/10.3103/s0146411618060081","mag":"2911804094"},"language":"en","primary_location":{"id":"doi:10.3103/s0146411618060081","is_oa":false,"landing_page_url":"https://doi.org/10.3103/s0146411618060081","pdf_url":null,"source":{"id":"https://openalex.org/S17203304","display_name":"Automatic Control and Computer Sciences","issn_l":"0146-4116","issn":["0146-4116","1558-108X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320267","host_organization_name":"Pleiades Publishing","host_organization_lineage":["https://openalex.org/P4310320267","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Pleiades Publishing","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Automatic Control and Computer Sciences","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/A5075186748","display_name":"Nicholas A. Nechval","orcid":"https://orcid.org/0000-0002-2234-8230"},"institutions":[{"id":"https://openalex.org/I91123046","display_name":"University of Latvia","ror":"https://ror.org/05g3mes96","country_code":"LV","type":"education","lineage":["https://openalex.org/I91123046"]}],"countries":["LV"],"is_corresponding":true,"raw_author_name":"N. A. Nechval","raw_affiliation_strings":["BVEF Research Institute, University of Latvia, LV-1050, Riga, Latvia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BVEF Research Institute, University of Latvia, LV-1050, Riga, Latvia","institution_ids":["https://openalex.org/I91123046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064436833","display_name":"Konstantin N. Nechval","orcid":"https://orcid.org/0009-0003-1876-5933"},"institutions":[{"id":"https://openalex.org/I24568809","display_name":"Transport and Telecommunication Institute","ror":"https://ror.org/01628w679","country_code":"LV","type":"education","lineage":["https://openalex.org/I24568809"]}],"countries":["LV"],"is_corresponding":false,"raw_author_name":"K. N. Nechval","raw_affiliation_strings":["Aviation Department, Transport and Telecommunication Institute, LV-1019, Riga, Latvia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aviation Department, Transport and Telecommunication Institute, LV-1019, Riga, Latvia","institution_ids":["https://openalex.org/I24568809"]}]},{"author_position":"last","author":{"id":null,"display_name":"G. Berzins","orcid":null},"institutions":[{"id":"https://openalex.org/I91123046","display_name":"University of Latvia","ror":"https://ror.org/05g3mes96","country_code":"LV","type":"education","lineage":["https://openalex.org/I91123046"]}],"countries":["LV"],"is_corresponding":false,"raw_author_name":"G. Berzins","raw_affiliation_strings":["BVEF Research Institute, University of Latvia, LV-1050, Riga, Latvia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BVEF Research Institute, University of Latvia, LV-1050, Riga, Latvia","institution_ids":["https://openalex.org/I91123046"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075186748"],"corresponding_institution_ids":["https://openalex.org/I91123046"],"apc_list":null,"apc_paid":null,"fwci":1.8998,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.86912029,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"52","issue":"6","first_page":"476","last_page":"488"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9955999851226807,"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"}},"topics":[{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9955999851226807,"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"}},{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9948999881744385,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9912999868392944,"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/weibull-distribution","display_name":"Weibull distribution","score":0.7867082357406616},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6683238744735718},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.6080148220062256},{"id":"https://openalex.org/keywords/order-statistic","display_name":"Order statistic","score":0.5974470973014832},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.5184604525566101},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5007212162017822},{"id":"https://openalex.org/keywords/percentile","display_name":"Percentile","score":0.48251745104789734},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.45816734433174133},{"id":"https://openalex.org/keywords/type-i-and-type-ii-errors","display_name":"Type I and type II errors","score":0.44252264499664307},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4319967031478882},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.4227682948112488},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.41927570104599},{"id":"https://openalex.org/keywords/sample-space","display_name":"Sample space","score":0.41307756304740906},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2757871747016907},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.115266352891922}],"concepts":[{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.7867082357406616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6683238744735718},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6080148220062256},{"id":"https://openalex.org/C44082924","wikidata":"https://www.wikidata.org/wiki/Q1767128","display_name":"Order statistic","level":2,"score":0.5974470973014832},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.5184604525566101},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5007212162017822},{"id":"https://openalex.org/C122048520","wikidata":"https://www.wikidata.org/wiki/Q2913954","display_name":"Percentile","level":2,"score":0.48251745104789734},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45816734433174133},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.44252264499664307},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4319967031478882},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4227682948112488},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.41927570104599},{"id":"https://openalex.org/C100279318","wikidata":"https://www.wikidata.org/wiki/Q467440","display_name":"Sample space","level":2,"score":0.41307756304740906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2757871747016907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.115266352891922},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3103/s0146411618060081","is_oa":false,"landing_page_url":"https://doi.org/10.3103/s0146411618060081","pdf_url":null,"source":{"id":"https://openalex.org/S17203304","display_name":"Automatic Control and Computer Sciences","issn_l":"0146-4116","issn":["0146-4116","1558-108X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320267","host_organization_name":"Pleiades Publishing","host_organization_lineage":["https://openalex.org/P4310320267","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Pleiades Publishing","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Automatic Control and Computer Sciences","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1963692958","https://openalex.org/W1967402487","https://openalex.org/W1970329499","https://openalex.org/W1976830048","https://openalex.org/W1997823124","https://openalex.org/W1998354248","https://openalex.org/W2026589428","https://openalex.org/W2040066666","https://openalex.org/W2042031536","https://openalex.org/W2071076722","https://openalex.org/W2072214560","https://openalex.org/W2120793724","https://openalex.org/W2137048831","https://openalex.org/W2279721067","https://openalex.org/W2343543415","https://openalex.org/W2498520162","https://openalex.org/W2579240251","https://openalex.org/W2768735944","https://openalex.org/W2924754085","https://openalex.org/W3003514415","https://openalex.org/W4234427264","https://openalex.org/W4248370127","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W3083898685","https://openalex.org/W2037820527","https://openalex.org/W1973754976","https://openalex.org/W189075692","https://openalex.org/W1550559433","https://openalex.org/W4220847856","https://openalex.org/W4220801072","https://openalex.org/W2328223263","https://openalex.org/W2379986491","https://openalex.org/W4390513996"],"abstract_inverted_index":{"Abstract":[0],"The":[1,19,104,159,191,212],"logical":[2],"purpose":[3],"for":[4,14,74,112],"a":[5,71,126,197],"statistical":[6,77,121],"tolerance":[7,81,114,161,218],"limit":[8],"is":[9,23,44,94,117,123,147,194,205,214,230],"to":[10,31,52,96,210,216,240],"predict":[11],"future":[12,28,91],"outcomes":[13,30,84],"some":[15],"(say,":[16],"production)":[17],"process.":[18],"coverage":[20],"value":[21],"\u03b3":[22],"the":[24,27,35,38,45,48,60,97,120,134,141,150,226,235],"percentage":[25,55],"of":[26,47,59,128,143,234],"process":[29],"be":[32,173],"captured":[33],"by":[34],"prediction,":[36],"and":[37,79,116,146,176,188,200,208],"confidence":[39],"level":[40],"(1":[41],"\u2013":[42],"\u03b1)":[43],"proportion":[46],"time":[49],"we":[50,221],"hope":[51],"capture":[53],"that":[54,130],"\u03b3.":[56],"Tolerance":[57],"limits":[58,82,162],"type":[61],"mentioned":[62],"above":[63],"are":[64,153],"considered":[65],"in":[66,90,232],"this":[67],"paper,":[68],"which":[69],"presents":[70],"new":[72],"technique":[73,105,193],"constructing":[75],"exact":[76,160],"(lower":[78],"upper)":[80],"on":[83,87,133,163,196],"(for":[85],"example,":[86],"order":[88,164],"statistics)":[89],"samples.":[92],"Attention":[93],"restricted":[95,215],"two-parameter":[98,236],"Weibull":[99,237],"distribution":[100],"under":[101,125],"parametric":[102],"uncertainty.":[103],"used":[106],"here":[107],"emphasizes":[108],"pivotal":[109,201],"quantities":[110],"relevant":[111],"obtaining":[113],"factors":[115],"applicable":[118,148],"whenever":[119],"problem":[122],"invariant":[124],"group":[127],"transformations":[129],"acts":[131],"transitively":[132],"parameter":[135],"space.":[136],"It":[137,204],"does":[138],"not":[139],"require":[140],"construction":[142],"any":[144],"tables":[145],"whether":[149],"experimental":[151],"data":[152],"complete":[154],"or":[155],"Type":[156],"II":[157],"censored.":[158],"statistics":[165],"associated":[166],"with":[167],"sampling":[168],"from":[169],"underlying":[170],"distributions":[171,243],"can":[172],"found":[174],"easily":[175],"quickly":[177],"making":[178],"tables,":[179],"simulation,":[180],"Monte":[181],"Carlo":[182],"estimated":[183],"percentiles,":[184],"special":[185],"computer":[186],"programs,":[187],"approximation":[189],"unnecessary.":[190],"proposed":[192,227],"based":[195],"probability":[198],"transformation":[199],"quantity":[202],"averaging.":[203],"conceptually":[206],"simple":[207],"easy":[209],"use.":[211],"discussion":[213],"one-sided":[217],"limits.":[219],"Finally,":[220],"give":[222],"numerical":[223],"examples,":[224],"where":[225],"analytical":[228],"methodology":[229],"illustrated":[231],"terms":[233],"distribution.":[238],"Applications":[239],"other":[241],"log-location-scale":[242],"could":[244],"follow":[245],"directly.":[246]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
