{"id":"https://openalex.org/W2120970819","doi":null,"title":"Bayesian Analysis for Longitudinal Semicontinuous Data","display_name":"Bayesian Analysis for Longitudinal Semicontinuous Data","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W2120970819","mag":"2120970819"},"language":"it","primary_location":{"id":"mag:2120970819","doi":null,"is_oa":false,"landing_page_url":"http://brb.nci.nih.gov/techreport/semicontinuous_final.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","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/A5047495561","display_name":"Pulak Ghosh","orcid":"https://orcid.org/0000-0003-4775-4179","observed_orcids":["https://orcid.org/0000-0003-4775-4179"]},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pulak Ghosh","raw_affiliation_strings":["Georgia State University,"],"raw_orcid":"https://orcid.org/0000-0003-4775-4179","affiliations":[{"raw_affiliation_string":"Georgia State University,","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039237147","display_name":"Paul S. Albert","orcid":"https://orcid.org/0000-0003-1658-1068","observed_orcids":["https://orcid.org/0000-0003-1658-1068"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paul S. Albert","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1658-1068","affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20088685,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9965999722480774,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.002899999963119626,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":9.999999747378752e-05,"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/zero-inflated-models","display_name":"zero-inflated models","score":0.9940000176429749},{"id":"https://openalex.org/keywords/nonparametric-bayesian-inference","display_name":"Bayesian nonparametrics","score":0.6669999957084656},{"id":"https://openalex.org/keywords/dirichlet-process-mixture-model","display_name":"Dirichlet process mixture model","score":0.8859999775886536},{"id":"https://openalex.org/keywords/penalized-splines","display_name":"penalized splines","score":0.7620000243186951},{"id":"https://openalex.org/keywords/acupuncture","display_name":"acupuncture","score":0.460999995470047},{"id":"https://openalex.org/keywords/chemotherapy-induced-nausea","display_name":"chemotherapy-induced nausea","score":0.9769999980926514},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"breast cancer","score":0.9279999732971191},{"id":"https://openalex.org/keywords/zero-inflated-poisson-regression","display_name":"zero-inflated Poisson regression","score":0.6299999952316284},{"id":"https://openalex.org/keywords/skewness","display_name":"skewness","score":0.5379999876022339}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5447614192962646},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.5124273896217346},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.4855884909629822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41934627294540405},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4192917048931122},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3467279076576233},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.34205174446105957},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.24614137411117554}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2120970819","doi":null,"is_oa":false,"landing_page_url":"http://brb.nci.nih.gov/techreport/semicontinuous_final.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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1494281971","https://openalex.org/W1592576206","https://openalex.org/W1965959053","https://openalex.org/W1967687583","https://openalex.org/W1972399593","https://openalex.org/W1977586926","https://openalex.org/W1989062675","https://openalex.org/W1995179111","https://openalex.org/W2015558203","https://openalex.org/W2027106381","https://openalex.org/W2048092992","https://openalex.org/W2053405531","https://openalex.org/W2055282927","https://openalex.org/W2057765075","https://openalex.org/W2058744048","https://openalex.org/W2059166063","https://openalex.org/W2069429561","https://openalex.org/W2083875149","https://openalex.org/W2091797506","https://openalex.org/W2093223772","https://openalex.org/W2102386709","https://openalex.org/W2117180217","https://openalex.org/W2127973942","https://openalex.org/W2142425347","https://openalex.org/W2151967501","https://openalex.org/W2162832722","https://openalex.org/W2167750957","https://openalex.org/W2185750390","https://openalex.org/W2237132745","https://openalex.org/W2330413962","https://openalex.org/W3123935820"],"related_works":["https://openalex.org/W7097432826","https://openalex.org/W1995065392","https://openalex.org/W2029319862","https://openalex.org/W2806758861","https://openalex.org/W2562729734","https://openalex.org/W2080948341","https://openalex.org/W2910366669","https://openalex.org/W2121388581","https://openalex.org/W2765370984","https://openalex.org/W3127600459"],"abstract_inverted_index":{"In":[0,46],"many":[1],"biomedical":[2],"applications,":[3],"researchers":[4],"encounter":[5],"semicontinuous":[6],"data":[7,9,17,33,85],"whereby":[8],"are":[10,18,24,146],"either":[11],"continuous":[12],"or":[13],"zero.":[14],"When":[15],"the":[16,22,38,44,95,125,129,133,144],"collected":[19],"over":[20],"time":[21],"observations":[23],"correlated.":[25],"Analysis":[26],"of":[27,30,40,54,97,128,135],"these":[28],"kind":[29],"longitudinal":[31,59,84],"semi-continuous":[32],"is":[34,122],"challenging":[35],"due":[36],"to":[37,82,131,148],"presence":[39],"strong":[41],"skewness":[42],"in":[43,57,91,109,143],"data.":[45],"this":[47],"paper,":[48],"we":[49,77,93],"develop":[50],"a":[51,58,63,79,87,150],"\u221eexible":[52],"class":[53],"zero-in\u221eated":[55],"models":[56],"setting.":[60],"Improving":[61],"on":[62,106],"likelihood-based":[64],"approach":[65,81],"(using":[66],"Monte-Carlo":[67],"EM)":[68],"that":[69],"was":[70],"proposed":[71],"by":[72],"Albert":[73],"and":[74,102],"Shen":[75],"(2005),":[76],"use":[78],"Bayesian":[80],"analyze":[83],"from":[86],"acupuncture":[88,101],"clinical":[89],"trial":[90],"which":[92],"compare":[94],"efiects":[96,141],"active":[98],"acupuncture,":[99],"sham":[100],"standard":[103],"medical":[104],"care":[105],"chemotherapy-induced":[107],"nausea":[108],"patients":[110],"being":[111],"treated":[112],"for":[113],"advanced":[114],"stage":[115],"breast":[116],"cancer.":[117],"A":[118],"penalized":[119],"spline":[120],"model":[121,130,145],"introduced":[123],"into":[124],"linear":[126],"predictor":[127],"explore":[132],"possibility":[134],"nonlinear":[136],"treatment":[137],"efiect.":[138],"The":[139],"subject-speciflc":[140],"involved":[142],"assumed":[147],"follow":[149],"nonparametric":[151],"Dirichlet":[152],"process":[153],"(DP)":[154],"mixture.":[155],"We":[156],"also":[157],"account":[158]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}