{"id":"https://openalex.org/W413132675","doi":"https://doi.org/10.31274/rtd-180813-13825","title":"Statistical analysis using finite mixtures of normal linear models","display_name":"Statistical analysis using finite mixtures of normal linear models","publication_year":1999,"publication_date":"1999-01-01","ids":{"openalex":"https://openalex.org/W413132675","doi":"https://doi.org/10.31274/rtd-180813-13825","mag":"413132675"},"language":"en","primary_location":{"id":"doi:10.31274/rtd-180813-13825","doi":"https://doi.org/10.31274/rtd-180813-13825","is_oa":false,"landing_page_url":"https://doi.org/10.31274/rtd-180813-13825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iowa State University, Digital Repository","raw_type":"dissertation"},"volume":null,"type":"dissertation","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/A5044354277","display_name":"Jianlin Jack Cheng","orcid":"https://orcid.org/0000-0003-0305-2853","observed_orcids":["https://orcid.org/0000-0003-0305-2853"]},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jianlin Cheng","raw_affiliation_strings":["Iowa State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University","institution_ids":["https://openalex.org/I173911158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5044354277"],"corresponding_institution_ids":["https://openalex.org/I173911158"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":1.0,"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":1.0,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.0,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.0,"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/mixture-models","display_name":"mixture models","score":0.9279999732971191},{"id":"https://openalex.org/keywords/subgroup-heterogeneity","display_name":"subgroup heterogeneity","score":0.7170000076293945},{"id":"https://openalex.org/keywords/parameter-identifiability","display_name":"parameter identifiability","score":0.800000011920929},{"id":"https://openalex.org/keywords/maximum-likelihood-estimation","display_name":"maximum likelihood estimation","score":0.8420000076293945},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.8460000157356262},{"id":"https://openalex.org/keywords/posterior-predictive-model-checking","display_name":"posterior predictive model checking","score":0.8370000123977661},{"id":"https://openalex.org/keywords/household-expenditure","display_name":"household expenditure","score":0.6480000019073486},{"id":"https://openalex.org/keywords/bangladesh","display_name":"Bangladesh","score":0.8629999756813049}],"concepts":[{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.9116088151931763},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6770055890083313},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6086028814315796},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.5912561416625977},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5400941371917725},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.5378678441047668},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5364184379577637},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.47839558124542236},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4709392786026001},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.42572343349456787},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40961718559265137},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4072652757167816},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.3333849310874939},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.28357911109924316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17096355557441711}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.31274/rtd-180813-13825","doi":"https://doi.org/10.31274/rtd-180813-13825","is_oa":false,"landing_page_url":"https://doi.org/10.31274/rtd-180813-13825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iowa State University, Digital Repository","raw_type":"dissertation"},{"id":"pmh:oai:dr.lib.iastate.edu:20.500.12876/65938","doi":null,"is_oa":false,"landing_page_url":"https://dr.lib.iastate.edu/handle/20.500.12876/65938","pdf_url":null,"source":{"id":"https://openalex.org/S4377196104","display_name":"Iowa State University Digital Repository (Iowa State University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I173911158","host_organization_name":"Iowa State University","host_organization_lineage":["https://openalex.org/I173911158"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"archive/lib.dr.iastate.edu/rtd/12557/r_9924708.pdf|||Fri Jan 14 19:24:21 UTC 2022","raw_type":"dissertation"},{"id":"pmh:oai:lib.dr.iastate.edu:rtd-13556","doi":null,"is_oa":false,"landing_page_url":"https://lib.dr.iastate.edu/rtd/12557","pdf_url":null,"source":{"id":"https://openalex.org/S4377196104","display_name":"Iowa State University Digital Repository (Iowa State University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I173911158","host_organization_name":"Iowa State University","host_organization_lineage":["https://openalex.org/I173911158"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Retrospective Theses and Dissertations","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W82247633","https://openalex.org/W306662213","https://openalex.org/W1494853941","https://openalex.org/W1579949713","https://openalex.org/W1582801283","https://openalex.org/W1584238677","https://openalex.org/W1638753340","https://openalex.org/W1660492679","https://openalex.org/W1966371185","https://openalex.org/W1966502880","https://openalex.org/W1967639437","https://openalex.org/W1978160697","https://openalex.org/W1978419774","https://openalex.org/W1981367467","https://openalex.org/W1992402718","https://openalex.org/W2000818742","https://openalex.org/W2005587766","https://openalex.org/W2011277999","https://openalex.org/W2017696952","https://openalex.org/W2018388286","https://openalex.org/W2027255954","https://openalex.org/W2036617620","https://openalex.org/W2040011853","https://openalex.org/W2042080635","https://openalex.org/W2042883129","https://openalex.org/W2045656233","https://openalex.org/W2046420182","https://openalex.org/W2048971218","https://openalex.org/W2049633694","https://openalex.org/W2053742104","https://openalex.org/W2058220245","https://openalex.org/W2068993558","https://openalex.org/W2083875149","https://openalex.org/W2091797506","https://openalex.org/W2093223772","https://openalex.org/W2096878708","https://openalex.org/W2097175338","https://openalex.org/W2106706098","https://openalex.org/W2111051773","https://openalex.org/W2118254160","https://openalex.org/W2121407732","https://openalex.org/W2130416410","https://openalex.org/W2135268685","https://openalex.org/W2138309709","https://openalex.org/W2146855196","https://openalex.org/W2147503863","https://openalex.org/W2147810983","https://openalex.org/W2148534890","https://openalex.org/W2152922857","https://openalex.org/W2160624840","https://openalex.org/W2163738067","https://openalex.org/W2166698530","https://openalex.org/W2168934042","https://openalex.org/W2334452549","https://openalex.org/W2526019368","https://openalex.org/W2531230719","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W7019684908","https://openalex.org/W156073098","https://openalex.org/W2161104570","https://openalex.org/W2282973946","https://openalex.org/W2182626357","https://openalex.org/W2147140316","https://openalex.org/W1497303833","https://openalex.org/W2518928548","https://openalex.org/W4392937177","https://openalex.org/W167579025"],"abstract_inverted_index":{"Finite":[0],"mixture":[1,71,97,116,156,240,248,334],"models":[2,47,54,157],"are":[3,48,99,107,118,129,144,184,261,279],"often":[4],"used":[5,185],"in":[6,74,81,94,149,269],"statistical":[7,123,201],"applications":[8],"when":[9,151],"the":[10,31,76,79,87,96,104,111,114,126,135,162,176,188,191,219,239,247,282,295,312],"population":[11,80,313],"under":[12,109],"study":[13],"is":[14,26,84,197,235,310],"believed":[15],"to":[16,29,33,60,101,122,168,186,194,209,246,256,263,339],"consist":[17],"of":[18,21,43,53,63,70,78,86,113,161,178,190,221,241,249,284,315,318,335],"a":[19,51,57,61,92,200,207,231],"number":[20],"heterogeneous":[22],"subpopulations,":[23],"but":[24],"it":[25],"not":[27,323],"possible":[28],"identify":[30],"subpopulation":[32,83],"which":[34,75,95,110],"an":[35,264],"individual":[36],"belongs.":[37],"In":[38,229],"this":[39,227,341],"thesis,":[40],"finite":[41,115,155,333],"mixtures":[42],"normal":[44],"linear":[45,292],"regression":[46,293],"explored":[49],"as":[50],"class":[52],"for":[55,125,226,237],"relating":[56],"response":[58],"variable":[59],"set":[62],"predictor":[64,89,105],"variables.":[65],"We":[66],"consider":[67],"two":[68,192,316],"classes":[69],"models:":[72],"those":[73,320,329],"proportion":[77],"each":[82],"independent":[85],"measured":[88],"variables,":[90],"and":[91,134,140,175,211,258,299,328],"second":[93],"proportions":[98,245],"allowed":[100],"depend":[102],"on":[103,326],"variables;Conditions":[106],"determined":[108],"parameters":[112,128],"model":[117,127,208,214,224,259],"identifiable.":[119],"Two":[120],"approaches":[121,193,255],"inference":[124,257],"reviewed:":[130],"maximum":[131],"likelihood":[132,163],"estimation":[133],"associated":[136],"large":[137,180],"sample":[138,181],"theory,":[139],"Bayesian":[141],"inference.":[142,195],"There":[143],"several":[145],"complications":[146],"that":[147,199,276,311,321,330],"arise":[148],"practice":[150],"analyzing":[152],"data":[153,210],"with":[154,171,243,251],"including":[158],"multiple":[159],"modes":[160,166],"function,":[164],"degenerate":[165],"corresponding":[167],"small":[169],"subpopulations":[170],"apparently":[172],"zero":[173],"variance,":[174],"failure":[177],"traditional":[179],"results.":[182],"Simulations":[183],"investigate":[187],"performance":[189],"It":[196],"important":[198],"analysis":[202],"go":[203],"beyond":[204],"just":[205],"fitting":[206],"include":[212],"some":[213],"assessment.":[215],"This":[216],"thesis":[217],"explores":[218],"use":[220],"posterior":[222,232],"predictive":[223,233],"checks":[225],"purpose.":[228],"particular":[230],"method":[234],"proposed":[236],"comparing":[238],"regressions":[242,250,336],"constant":[244],"nonconstant":[252],"proportions;The":[253],"various":[254],"assessment":[260],"applied":[262],"example":[265],"concerning":[266],"household":[267],"expenditures":[268],"Bangladesh.":[270],"An":[271],"economic":[272],"hypothesis":[273],"there":[274],"suggests":[275],"more":[277],"resources":[278],"spent":[280],"ensuring":[281],"health":[283,302],"male":[285,298],"rather":[286],"than":[287],"female":[288,300],"children.":[289],"A":[290],"simple":[291],"explaining":[294],"difference":[296],"between":[297],"child":[301],"finds":[303],"no":[304],"significant":[305],"predictors.":[306],"One":[307],"plausible":[308],"explanation":[309],"consists":[314],"types":[317],"households,":[319],"do":[322],"discriminate":[324],"based":[325],"gender":[327],"do.":[331],"The":[332],"allows":[337],"us":[338],"address":[340],"hypothesis.":[342]},"counts_by_year":[],"updated_date":"2026-09-26T08:19:03.415552","created_date":"2016-06-24T00:00:00"}
