{"id":"https://openalex.org/W2303140368","doi":null,"title":"On Quantifying the Accuracy of Maximum Likelihood Estimation of Participant Reliability in Social Sensing","display_name":"On Quantifying the Accuracy of Maximum Likelihood Estimation of Participant Reliability in Social Sensing","publication_year":2011,"publication_date":"2011-01-01","ids":{"openalex":"https://openalex.org/W2303140368","mag":"2303140368"},"language":"en","primary_location":{"id":"mag:2303140368","doi":null,"is_oa":false,"landing_page_url":"http://charuaggarwal.net/sensetrust.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5100391517","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0002-9599-8023","observed_orcids":["https://orcid.org/0000-0002-9599-8023"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Wang","raw_affiliation_strings":["***University of Illinois at Urbana-Champaign"],"raw_orcid":"https://orcid.org/0000-0002-9599-8023","affiliations":[{"raw_affiliation_string":"***University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022713560","display_name":"Lance Kaplan","orcid":"https://orcid.org/0000-0002-3627-4471","observed_orcids":["https://orcid.org/0000-0002-3627-4471"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lance Kaplan","raw_affiliation_strings":["***University of Illinois at Urbana-Champaign"],"raw_orcid":"https://orcid.org/0000-0002-3627-4471","affiliations":[{"raw_affiliation_string":"***University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028089542","display_name":"Char\u0173 C. Aggarwal","orcid":"https://orcid.org/0000-0003-2579-7581","observed_orcids":["https://orcid.org/0000-0003-2579-7581"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Char\u0173 C. Aggarwal","raw_affiliation_strings":["IBM"],"raw_orcid":"https://orcid.org/0000-0003-2579-7581","affiliations":[{"raw_affiliation_string":"IBM","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.4583,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.95734281,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":87,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T13309","display_name":"Reliability and Agreement in Measurement","score":9.999999747378752e-05,"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/social-sensing","display_name":"social sensing","score":0.9610000252723694},{"id":"https://openalex.org/keywords/maximum-likelihood-estimation","display_name":"maximum likelihood estimation","score":0.9559999704360962},{"id":"https://openalex.org/keywords/confidence-intervals","display_name":"confidence intervals","score":0.843999981880188},{"id":"https://openalex.org/keywords/cramer-rao-lower-bound","display_name":"Cramer-Rao lower bound","score":0.9139999747276306},{"id":"https://openalex.org/keywords/asymptotic-normality","display_name":"asymptotic normality","score":0.8799999952316284}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7908077836036682},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.7233232259750366},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.6490613222122192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5693556070327759},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.533923864364624},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5165585279464722},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.47640693187713623},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.46563947200775146},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45420241355895996},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.4393141269683838},{"id":"https://openalex.org/C61420037","wikidata":"https://www.wikidata.org/wiki/Q7316301","display_name":"Restricted maximum likelihood","level":3,"score":0.4254212975502014},{"id":"https://openalex.org/C191462741","wikidata":"https://www.wikidata.org/wiki/Q6795902","display_name":"Maximum likelihood sequence estimation","level":3,"score":0.420928955078125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34635084867477417},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3014937937259674},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13243794441223145},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2303140368","doi":null,"is_oa":false,"landing_page_url":"http://charuaggarwal.net/sensetrust.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1648420042","https://openalex.org/W1667830255","https://openalex.org/W1725819718","https://openalex.org/W2003684739","https://openalex.org/W2049633694","https://openalex.org/W2073545563","https://openalex.org/W2118388899","https://openalex.org/W2130254564","https://openalex.org/W2137242774","https://openalex.org/W2138621811","https://openalex.org/W2144242878","https://openalex.org/W2155189155","https://openalex.org/W2159296364","https://openalex.org/W2799002609"],"related_works":["https://openalex.org/W2320505878","https://openalex.org/W2142687577","https://openalex.org/W2181061855","https://openalex.org/W1423779789","https://openalex.org/W7098443960","https://openalex.org/W4253387571","https://openalex.org/W2224753198","https://openalex.org/W2522492899","https://openalex.org/W2038946499","https://openalex.org/W2051688597"],"abstract_inverted_index":{"This":[0,114],"paper":[1,115],"presents":[2,116],"a":[3,29,76,99],"condence":[4,133],"interval":[5,134],"quantication":[6,160],"of":[7,11,25,52,86,103,105,124,142,150,177],"maximum":[8,100,126,143],"likelihood":[9,67,101,127,144],"estimation":[10,128,145,156,172],"participant":[12],"reliability":[13,104],"in":[14,43,48,129],"social":[15,26,44,91,130],"sensing":[16,27,45,92,181],"applications.":[17],"The":[18,158],"work":[19],"is":[20,73,80,135,162],"motivated":[21],"by":[22,137],"the":[23,36,49,66,84,112,122,139,148,155,170],"emergence":[24],"as":[28],"data":[30,37],"collection":[31,38],"paradigm,":[32],"where":[33],"humans":[34,61],"perform":[35],"tasks.":[39],"A":[40,132],"key":[41],"challenge":[42],"applications":[46],"lies":[47],"uncertain":[50],"nature":[51],"human":[53],"measurements.":[54],"Unlike":[55],"well-calibrated":[56],"and":[57,65,108,146,165],"well-tested":[58],"infrastructure":[59],"sensors,":[60],"are":[62,71],"less":[63],"reliable,":[64],"that":[68,120],"participants\u2019":[69],"measurements":[70],"correct":[72],"often":[74],"unknown":[75],"priori.":[77],"Hence,":[78],"it":[79],"hard":[81],"to":[82,167],"estimate":[83],"accuracy":[85,123],"conclusions":[87],"made":[88],"based":[89],"on":[90],"data.":[93,113],"In":[94],"previous":[95],"work,":[96],"we":[97],"developed":[98],"estimator":[102],"both":[106],"participants":[107,178],"facts":[109],"concluded":[110],"from":[111],"an":[117],"analytically-founded":[118],"bound":[119,152,169],"quanties":[121],"such":[125],"sensing.":[131],"derived":[136],"leveraging":[138],"asymptotic":[140],"normality":[141],"computing":[147],"approximation":[149],"Cramer-Rao":[151],"(CRB)":[153],"for":[154],"parameters.":[157],"proposed":[159],"approach":[161],"empirically":[163],"validated":[164],"shown":[166],"accurately":[168],"actual":[171],"error":[173],"given":[174],"sucient":[175],"number":[176],"under":[179],"dierent":[180],"topologies.":[182]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":13},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":5}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
