{"id":"https://openalex.org/W2298185854","doi":"https://doi.org/10.1109/tit.2017.2786345","title":"Semiparametric Two-Component Mixture Models When One Component Is Defined Through Linear Constraints","display_name":"Semiparametric Two-Component Mixture Models When One Component Is Defined Through Linear Constraints","publication_year":2017,"publication_date":"2017-12-22","ids":{"openalex":"https://openalex.org/W2298185854","doi":"https://doi.org/10.1109/tit.2017.2786345","mag":"2298185854"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2017.2786345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2017.2786345","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1603.05694","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Diaa Al Mohamad","orcid":"https://orcid.org/0000-0002-8810-8571"},"institutions":[{"id":"https://openalex.org/I2800006345","display_name":"Leiden University Medical Center","ror":"https://ror.org/05xvt9f17","country_code":"NL","type":"funder","lineage":["https://openalex.org/I2800006345"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Diaa Al Mohamad","raw_affiliation_strings":["Department of Bioinformatics and Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-8810-8571","affiliations":[{"raw_affiliation_string":"Department of Bioinformatics and Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands","institution_ids":["https://openalex.org/I2800006345"]}]},{"author_position":"last","author":{"id":null,"display_name":"Assia Boumahdaf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Assia Boumahdaf","raw_affiliation_strings":["Fritlink, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fritlink, Paris, France","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3988,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68954386,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"64","issue":"2","first_page":"795","last_page":"830"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9818999767303467,"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":0.9818999767303467,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.002300000051036477,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.002199999988079071,"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/identifiability","display_name":"Identifiability","score":0.6992999911308289},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6729999780654907},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5734999775886536},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5389000177383423},{"id":"https://openalex.org/keywords/semiparametric-model","display_name":"Semiparametric model","score":0.5067999958992004},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5034000277519226},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4706999957561493},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.4375},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.41999998688697815}],"concepts":[{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.6992999911308289},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6729999780654907},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6504999995231628},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5734999775886536},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5529000163078308},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5389000177383423},{"id":"https://openalex.org/C78297888","wikidata":"https://www.wikidata.org/wiki/Q7449607","display_name":"Semiparametric model","level":3,"score":0.5067999958992004},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5034000277519226},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4706999957561493},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4691999852657318},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.4375},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.41999998688697815},{"id":"https://openalex.org/C19539793","wikidata":"https://www.wikidata.org/wiki/Q7449609","display_name":"Semiparametric regression","level":3,"score":0.4056999981403351},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.3977000117301941},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3635999858379364},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.34360000491142273},{"id":"https://openalex.org/C6802819","wikidata":"https://www.wikidata.org/wiki/Q1072174","display_name":"Linear system","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C49344536","wikidata":"https://www.wikidata.org/wiki/Q726441","display_name":"Cauchy distribution","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.2939000129699707},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C55660270","wikidata":"https://www.wikidata.org/wiki/Q5164377","display_name":"Constrained optimization","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C203223496","wikidata":"https://www.wikidata.org/wiki/Q4681344","display_name":"Additive model","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.25870001316070557},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25440001487731934}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tit.2017.2786345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2017.2786345","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1603.05694","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1603.05694","pdf_url":"https://arxiv.org/pdf/1603.05694","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"},{"id":"pmh:oai:arXiv.org:1606.08535","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1606.08535","pdf_url":"https://arxiv.org/pdf/1606.08535","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:1603.05694","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1603.05694","pdf_url":"https://arxiv.org/pdf/1603.05694","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W66363099","https://openalex.org/W152055444","https://openalex.org/W845356973","https://openalex.org/W1460189015","https://openalex.org/W1481794564","https://openalex.org/W1516900628","https://openalex.org/W1558034244","https://openalex.org/W1658614109","https://openalex.org/W1901230542","https://openalex.org/W1964703702","https://openalex.org/W1966018765","https://openalex.org/W1974234363","https://openalex.org/W1974642946","https://openalex.org/W1976475761","https://openalex.org/W2015569623","https://openalex.org/W2015700462","https://openalex.org/W2056199992","https://openalex.org/W2063033621","https://openalex.org/W2063181603","https://openalex.org/W2065527440","https://openalex.org/W2069501991","https://openalex.org/W2073667121","https://openalex.org/W2074089196","https://openalex.org/W2075354852","https://openalex.org/W2078912995","https://openalex.org/W2084238990","https://openalex.org/W2085199559","https://openalex.org/W2090458642","https://openalex.org/W2091262648","https://openalex.org/W2093064776","https://openalex.org/W2100169259","https://openalex.org/W2110065044","https://openalex.org/W2110442235","https://openalex.org/W2171074980","https://openalex.org/W2258141526","https://openalex.org/W2335614792","https://openalex.org/W2501819379","https://openalex.org/W2518143623","https://openalex.org/W2964202932","https://openalex.org/W4248081355","https://openalex.org/W6625253818","https://openalex.org/W6633964979"],"related_works":[],"abstract_inverted_index":{"We":[0,124,214],"propose":[1,125],"a":[2,5,34,96,129,133,185,226],"structure":[3,53],"of":[4,33,54,75,84,113,197,246],"semiparametric":[6,86,97],"two-component":[7,35],"mixture":[8,36,87,98],"model":[9,37,88,99,147],"when":[10,45,103,148,229],"one":[11],"component":[12,41,106,140],"is":[13,18,42,49,117,156,177,192],"parametric":[14,105,115],"and":[15,78,82,183,208,237,243],"the":[16,52,55,64,68,73,85,104,111,114,138,146,172,190,198,218,230,241,244],"other":[17],"defined":[19],"through":[20],"linear":[21,135,154,160,227],"constraints":[22,165,168,170,231],"on":[23,51,179,235],"either":[24,118,157],"its":[25,29],"distribution":[26],"function":[27],"or":[28,110,121,166],"quantile":[30],"measure.":[31],"Estimation":[32],"with":[38],"an":[39],"unknown":[40,56,108,139],"very":[43,119,122],"difficult":[44],"no":[46],"particular":[47],"assumption":[48,60],"made":[50],"component.":[57],"A":[58],"symmetry":[59],"was":[61],"used":[62],"in":[63,126,141],"literature":[65],"to":[66,131,143,205],"simplify":[67],"estimation.":[69],"Such":[70],"method":[71,130,176],"has":[72,107,184,225],"advantage":[74],"producing":[76],"consistent":[77,207],"asymptotically":[79,209],"normal":[80,210],"estimators,":[81],"identifiability":[83],"becomes":[89],"tractable.":[90],"Still,":[91],"existing":[92,149],"methods,":[93],"which":[94],"estimate":[95,145],"have":[100],"their":[101],"limits":[102],"parameters":[109],"proportion":[112],"part":[116],"high":[120],"low.":[123],"this":[127],"paper":[128],"incorporate":[132],"prior":[134],"information":[136,155],"about":[137],"order":[142],"better":[144],"estimation":[150],"methods":[151],"fail.":[152],"This":[153],"translated":[158],"by":[159],"constraints,":[161],"such":[162],"as":[163],"moment-type":[164],"L-moments":[167],"(linear":[169],"over":[171,194],"quantile).":[173],"The":[174,200],"new":[175],"based":[178],"$\\varphi":[180],"-$":[181],"divergences":[182],"non":[186],"classical":[187],"form":[188],"since":[189],"minimization":[191],"carried":[193],"both":[195],"arguments":[196],"divergence.":[199],"resulting":[201],"estimators":[202],"are":[203,232],"proved":[204],"be":[206],"under":[211],"standard":[212],"assumptions.":[213],"show":[215],"that":[216],"using":[217],"Pearson's":[219],"$\\chi":[220],"^{2}$":[221],"divergence":[222],"our":[223,247],"algorithm":[224],"complexity":[228],"moment-type.":[233],"Simulations":[234],"univariate":[236],"multivariate":[238],"mixtures":[239],"demonstrate":[240],"viability":[242],"interest":[245],"novel":[248],"approach.":[249]},"counts_by_year":[{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2016-06-24T00:00:00"}
