{"id":"https://openalex.org/W1504480307","doi":null,"title":"Generalized Linear Models and Extensions","display_name":"Generalized Linear Models and Extensions","publication_year":2012,"publication_date":"2012-01-01","ids":{"openalex":"https://openalex.org/W1504480307","mag":"1504480307"},"language":"en","primary_location":{"id":"mag:3149832734","doi":null,"is_oa":false,"landing_page_url":"https://ideas.repec.org/b/tsj/spbook/glmext.html","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","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/A5059616971","display_name":"James W. Hardin","orcid":"https://orcid.org/0000-0003-0506-5500","observed_orcids":["https://orcid.org/0000-0003-0506-5500"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"James W. Hardin","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-0506-5500","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5000669585","display_name":"Joseph M. Hilbe","orcid":"https://orcid.org/0000-0002-4626-7961","observed_orcids":["https://orcid.org/0000-0002-4626-7961"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joseph M. Hilbe","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4626-7961","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":71.7754,"has_fulltext":false,"cited_by_count":849,"citation_normalized_percentile":{"value":0.99958236,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"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":0.9150000214576721,"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":0.9150000214576721,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.020800000056624413,"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/T13141","display_name":"Statistical Methods and Applications","score":0.018400000408291817,"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/generalized-linear-models","display_name":"generalized linear models","score":1.0},{"id":"https://openalex.org/keywords/exponential-family","display_name":"exponential family","score":0.8790000081062317},{"id":"https://openalex.org/keywords/non-gaussian-response","display_name":"non-Gaussian response","score":0.7300000190734863},{"id":"https://openalex.org/keywords/discrete-response","display_name":"discrete response","score":0.7300000190734863},{"id":"https://openalex.org/keywords/logit-model","display_name":"logit model","score":0.8009999990463257},{"id":"https://openalex.org/keywords/probit-analysis","display_name":"probit model","score":0.9929999709129333},{"id":"https://openalex.org/keywords/poisson-regression","display_name":"Poisson regression","score":0.9769999980926514},{"id":"https://openalex.org/keywords/maximum-likelihood-estimation","display_name":"maximum likelihood estimation","score":0.8560000061988831},{"id":"https://openalex.org/keywords/fisher-scoring","display_name":"Fisher scoring","score":0.9599999785423279},{"id":"https://openalex.org/keywords/iteratively-reweighted-least-squares","display_name":"iteratively reweighted least squares","score":0.996999979019165},{"id":"https://openalex.org/keywords/standard-error-estimation","display_name":"standard error estimation","score":0.7639999985694885},{"id":"https://openalex.org/keywords/robust-standard-errors","display_name":"robust standard errors","score":0.9670000076293945},{"id":"https://openalex.org/keywords/clustered-standard-errors","display_name":"clustered standard errors","score":0.7260000109672546},{"id":"https://openalex.org/keywords/bootstrap-method","display_name":"bootstrapping","score":0.9599999785423279},{"id":"https://openalex.org/keywords/jackknife","display_name":"jackknife","score":0.9340000152587891}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7673187255859375},{"id":"https://openalex.org/C177599991","wikidata":"https://www.wikidata.org/wiki/Q3706279","display_name":"Deviance (statistics)","level":2,"score":0.6995641589164734},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.6988878846168518},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.68011474609375},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5648643970489502},{"id":"https://openalex.org/C114494560","wikidata":"https://www.wikidata.org/wiki/Q4914493","display_name":"Binomial regression","level":3,"score":0.4790990650653839},{"id":"https://openalex.org/C91025261","wikidata":"https://www.wikidata.org/wiki/Q7269460","display_name":"Quasi-likelihood","level":4,"score":0.4291277527809143},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4257733225822449},{"id":"https://openalex.org/C199335787","wikidata":"https://www.wikidata.org/wiki/Q743364","display_name":"Negative binomial distribution","level":3,"score":0.4218381345272064},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.31673216819763184},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.08555853366851807}],"mesh":[],"locations_count":5,"locations":[{"id":"mag:3149832734","doi":null,"is_oa":false,"landing_page_url":"https://ideas.repec.org/b/tsj/spbook/glmext.html","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},{"id":"pmh:oai:RePEc:tsj:spbook:glmext","doi":null,"is_oa":false,"landing_page_url":"https://www.stata-press.com/data/glmext3.html","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,"listed_in":[],"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":"book"},{"id":"pmh:oai:aleph.bib-bvb.de:BVB01-015579559","doi":null,"is_oa":false,"landing_page_url":"http://bvbr.bib-bvb.de:8991/F?func=service&amp;doc_library=BVB01&amp;local_base=BVB01&amp;doc_number=015579559&amp;sequence=000002&amp;line_number=0001&amp;func_code=DB_RECORDS&amp;service_type=MEDIA","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:aleph.bib-bvb.de:BVB01-025050582","doi":null,"is_oa":false,"landing_page_url":"http://bvbr.bib-bvb.de:8991/F?func=service&amp;doc_library=BVB01&amp;local_base=BVB01&amp;doc_number=025050582&amp;sequence=000002&amp;line_number=0001&amp;func_code=DB_RECORDS&amp;service_type=MEDIA","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:1504480307","doi":null,"is_oa":false,"landing_page_url":"https://ci.nii.ac.jp/ncid/BA81884142","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":0,"referenced_works":[],"related_works":["https://openalex.org/W117592956","https://openalex.org/W4206809382","https://openalex.org/W3149832734","https://openalex.org/W2329094530","https://openalex.org/W2135893344","https://openalex.org/W302225845","https://openalex.org/W7096250436","https://openalex.org/W3163593653","https://openalex.org/W4212945427","https://openalex.org/W2021347741"],"abstract_inverted_index":{"Generalized":[0],"linear":[1,5],"models":[2,8,45],"(GLMs)":[3],"extend":[4],"regression":[6],"to":[7],"with":[9,62,164],"a":[10,141],"non-Gaussian,":[11],"or":[12],"even":[13],"discrete,":[14],"response.":[15],"GLM":[16],"theory":[17,94],"is":[18,140],"predicated":[19],"on":[20],"the":[21,32,81,99,106,117,170],"exponential":[22,107],"family":[23,113],"of":[24,80,96,105,111,114,119,137,143,155,169],"distributions\u2014a":[25],"class":[26],"so":[27],"rich":[28],"that":[29],"it":[30],"includes":[31],"commonly":[33],"used":[34],"logit,":[35],"probit,":[36],"and":[37,76,91,116,124,173],"Poisson":[38],"distributions.":[39],"Although":[40],"one":[41],"can":[42],"fit":[43],"these":[44],"in":[46],"Stata":[47],"by":[48],"using":[49],"specialized":[50],"commands":[51],"(for":[52],"example,":[53,70],"logit":[54,56],"for":[55],"models),":[57],"fitting":[58],"them":[59],"as":[60],"GLMs":[61,101],"Stata\u2019s":[63],"glm":[64],"command":[65],"offers":[66],"some":[67],"advantages.":[68],"For":[69],"model":[71],"diagnostics":[72],"may":[73],"be":[74],"calculated":[75],"interpreted":[77],"similarly":[78],"regardless":[79],"assumed":[82],"distribution.":[83],"This":[84],"text":[85],"thoroughly":[86],"covers":[87],"GLMs,":[88],"both":[89],"theoretically":[90],"computationally.":[92],"The":[93,127,149],"consists":[95],"showing":[97],"how":[98,130],"various":[100],"are":[102],"special":[103],"cases":[104],"family,":[108],"general":[109],"properties":[110],"this":[112],"distributions,":[115],"derivation":[118],"maximum":[120],"likelihood":[121],"(ML)":[122],"estimators":[123],"standard":[125,157],"errors.":[126],"book":[128],"shows":[129],"iteratively":[131],"reweighted":[132],"least":[133],"squares,":[134],"another":[135],"method":[136],"parameter":[138],"estimation,":[139],"consequence":[142],"ML":[144],"estimation":[145],"via":[146],"Fisher":[147],"scoring.":[148],"authors":[150],"also":[151],"discuss":[152],"different":[153],"methods":[154,163],"estimating":[156],"errors,":[158],"including":[159],"robust":[160,162],"methods,":[161],"clustering,":[165],"Newey\u2013West,":[166],"outer":[167],"product":[168],"gradient,":[171],"bootstrap,":[172],"jackknife.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":37},{"year":2020,"cited_by_count":39},{"year":2019,"cited_by_count":48},{"year":2018,"cited_by_count":29},{"year":2017,"cited_by_count":42},{"year":2016,"cited_by_count":52},{"year":2015,"cited_by_count":68},{"year":2014,"cited_by_count":63},{"year":2013,"cited_by_count":57},{"year":2012,"cited_by_count":61}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2016-06-24T00:00:00"}
