{"id":"https://openalex.org/W2317967562","doi":"https://doi.org/10.1155/2016/1648462","title":"Assessing Heterogeneity for Factor Analysis Model with Continuous and Ordinal Outcomes","display_name":"Assessing Heterogeneity for Factor Analysis Model with Continuous and Ordinal Outcomes","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2317967562","doi":"https://doi.org/10.1155/2016/1648462","mag":"2317967562"},"language":"en","primary_location":{"id":"doi:10.1155/2016/1648462","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2016/1648462","pdf_url":"https://downloads.hindawi.com/journals/jam/2016/1648462.pdf","source":{"id":"https://openalex.org/S190082376","display_name":"Journal of Applied Mathematics","issn_l":"1110-757X","issn":["1110-757X","1687-0042"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Applied Mathematics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://downloads.hindawi.com/journals/jam/2016/1648462.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085501533","display_name":"Ye\u2010Mao Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ye-Mao Xia","raw_affiliation_strings":["Department of Applied Mathematics, Nanjing Forestry University, Nanjing, Jiangsu 210037, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Nanjing Forestry University, Nanjing, Jiangsu 210037, China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025356141","display_name":"Jianwei Gou","orcid":null},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian-Wei Gou","raw_affiliation_strings":["Department of Applied Mathematics, Nanjing Forestry University, Nanjing, Jiangsu 210037, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Nanjing Forestry University, Nanjing, Jiangsu 210037, China","institution_ids":["https://openalex.org/I167027274"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085501533"],"corresponding_institution_ids":["https://openalex.org/I167027274"],"apc_list":{"value":1025,"currency":"USD","value_usd":1025},"apc_paid":{"value":1025,"currency":"USD","value_usd":1025},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01198233,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2016","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9962000250816345,"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":0.9836000204086304,"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/gibbs-sampling","display_name":"Gibbs sampling","score":0.6304505467414856},{"id":"https://openalex.org/keywords/ordinal-data","display_name":"Ordinal data","score":0.6106621026992798},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6048840284347534},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5782739520072937},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5001540184020996},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4985964298248291},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.495263010263443},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46519482135772705},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4558469355106354},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.45376813411712646},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.44922178983688354},{"id":"https://openalex.org/keywords/factor-analysis","display_name":"Factor analysis","score":0.4471384882926941},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.44047749042510986},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4105723202228546}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6304505467414856},{"id":"https://openalex.org/C85461838","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal data","level":2,"score":0.6106621026992798},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6048840284347534},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5782739520072937},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5001540184020996},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4985964298248291},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.495263010263443},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46519482135772705},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4558469355106354},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.45376813411712646},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.44922178983688354},{"id":"https://openalex.org/C10879293","wikidata":"https://www.wikidata.org/wiki/Q726474","display_name":"Factor analysis","level":2,"score":0.4471384882926941},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.44047749042510986},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4105723202228546}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1155/2016/1648462","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2016/1648462","pdf_url":"https://downloads.hindawi.com/journals/jam/2016/1648462.pdf","source":{"id":"https://openalex.org/S190082376","display_name":"Journal of Applied Mathematics","issn_l":"1110-757X","issn":["1110-757X","1687-0042"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Applied Mathematics","raw_type":"journal-article"},{"id":"pmh:oai:CULeuclid:euclid.jam/1460554509","is_oa":false,"landing_page_url":"http://projecteuclid.org/euclid.jam/1460554509","pdf_url":null,"source":{"id":"https://openalex.org/S4306400787","display_name":"Project Euclid (Cornell University)","issn_l":null,"issn":null,"is_oa":false,"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":"","raw_type":"Text"},{"id":"pmh:oai:RePEc:hin:jnljam:1648462","is_oa":false,"landing_page_url":"http://downloads.hindawi.com/journals/JAM/2016/1648462.xml","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"article"},{"id":"pmh:oai:doaj.org/article:0b764818b2344e5eafe8cdd376ea9f09","is_oa":true,"landing_page_url":"https://doaj.org/article/0b764818b2344e5eafe8cdd376ea9f09","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Applied Mathematics, Vol 2016 (2016)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2016/1648462","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2016/1648462","pdf_url":"https://downloads.hindawi.com/journals/jam/2016/1648462.pdf","source":{"id":"https://openalex.org/S190082376","display_name":"Journal of Applied Mathematics","issn_l":"1110-757X","issn":["1110-757X","1687-0042"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Applied Mathematics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2559054032","display_name":null,"funder_award_id":"11471161","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5732515703","display_name":null,"funder_award_id":"163101004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320312290","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2317967562.pdf","grobid_xml":"https://content.openalex.org/works/W2317967562.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1967396577","https://openalex.org/W1967456918","https://openalex.org/W1967687583","https://openalex.org/W1969830729","https://openalex.org/W1980782084","https://openalex.org/W1982636789","https://openalex.org/W1984244135","https://openalex.org/W1985584398","https://openalex.org/W1985961306","https://openalex.org/W1996786829","https://openalex.org/W2002185523","https://openalex.org/W2003684724","https://openalex.org/W2029774915","https://openalex.org/W2030155614","https://openalex.org/W2032917146","https://openalex.org/W2033765726","https://openalex.org/W2034938024","https://openalex.org/W2037834999","https://openalex.org/W2051378554","https://openalex.org/W2055801024","https://openalex.org/W2056760934","https://openalex.org/W2061735246","https://openalex.org/W2065392216","https://openalex.org/W2069429561","https://openalex.org/W2070047497","https://openalex.org/W2071983892","https://openalex.org/W2072169887","https://openalex.org/W2079501320","https://openalex.org/W2084925598","https://openalex.org/W2087933148","https://openalex.org/W2089773781","https://openalex.org/W2091797506","https://openalex.org/W2108306139","https://openalex.org/W2113779710","https://openalex.org/W2125185478","https://openalex.org/W2138309709","https://openalex.org/W2147562946","https://openalex.org/W2148534890","https://openalex.org/W2149767430","https://openalex.org/W2152977846","https://openalex.org/W2166698530","https://openalex.org/W3015217870","https://openalex.org/W4211177544","https://openalex.org/W4234603045"],"related_works":["https://openalex.org/W94839547","https://openalex.org/W3151509436","https://openalex.org/W2018595850","https://openalex.org/W1564347458","https://openalex.org/W605603493","https://openalex.org/W2123736748","https://openalex.org/W3160707851","https://openalex.org/W2094015288","https://openalex.org/W4231537836","https://openalex.org/W2347000526"],"abstract_inverted_index":{"Factor":[0],"analysis":[1,43,86,112],"models":[2,24],"with":[3,70,88],"continuous":[4,89],"and":[5,19,36,90],"ordinal":[6,91],"responses":[7],"are":[8,47,57,149],"a":[9,80],"useful":[10],"tool":[11],"for":[12,59,84],"assessing":[13],"relations":[14],"between":[15],"the":[16,41,54,60,101,104,117,129,135,144,153,156],"latent":[17],"variables":[18],"mixed":[20],"observed":[21],"responses.":[22],"These":[23],"have":[25],"been":[26],"successfully":[27],"applied":[28],"to":[29,66,99,125,142,151],"many":[30],"different":[31],"fields,":[32],"including":[33],"behavioral,":[34],"educational,":[35],"social-psychological":[37],"sciences.":[38],"However,":[39],"within":[40,49],"Bayesian":[42,81,110],"framework,":[44],"most":[45],"developments":[46],"constrained":[48],"parametric":[50],"families,":[51],"of":[52,62,103,137,155],"which":[53],"particular":[55],"distributions":[56,102],"specified":[58],"parameters":[61],"interest.":[63],"This":[64],"leads":[65],"difficulty":[67],"in":[68],"dealing":[69],"outliers":[71],"and/or":[72,106],"distribution":[73],"deviations.":[74],"In":[75],"this":[76],"paper,":[77],"we":[78],"propose":[79],"semiparametric":[82],"modeling":[83],"factor":[85],"model":[87,100,133],"variables.":[92],"A":[93],"truncated":[94],"stick-breaking":[95],"prior":[96],"is":[97,113,123,140],"used":[98],"intercept":[105],"covariance":[107],"structural":[108],"parameters.":[109],"posterior":[111],"carried":[114],"out":[115],"through":[116],"simulation-based":[118],"method.":[119],"Blocked":[120],"Gibbs":[121],"sampler":[122],"implemented":[124],"draw":[126],"observations":[127],"from":[128],"complicated":[130],"posterior.":[131],"For":[132],"selection,":[134],"logarithm":[136],"pseudomarginal":[138],"likelihood":[139],"developed":[141],"compare":[143],"competing":[145],"models.":[146],"Empirical":[147],"results":[148],"presented":[150],"illustrate":[152],"application":[154],"methodology.":[157]},"counts_by_year":[],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}
