{"id":"https://openalex.org/W2944752829","doi":null,"title":"Conformal prediction for exponential families and generalized linear models","display_name":"Conformal prediction for exponential families and generalized linear models","publication_year":2019,"publication_date":"2019-05-09","ids":{"openalex":"https://openalex.org/W2944752829","mag":"2944752829"},"language":"en","primary_location":{"id":"mag:2944752829","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1905.03657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1905.03657","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045885585","display_name":"Daniel J. Eck","orcid":"https://orcid.org/0000-0002-2571-5331","observed_orcids":["https://orcid.org/0000-0002-2571-5331"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel J. Eck","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2571-5331","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080170176","display_name":"Forrest W. Crawford","orcid":"https://orcid.org/0000-0002-0046-0547","observed_orcids":["https://orcid.org/0000-0002-0046-0547"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Forrest W. Crawford","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-0046-0547","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9179999828338623,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9179999828338623,"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/T13500","display_name":"Probability and Statistical Research","score":0.02070000022649765,"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":0.013500000350177288,"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/conformal-prediction","display_name":"conformal prediction","score":0.9950000047683716},{"id":"https://openalex.org/keywords/generalized-linear-models","display_name":"generalized linear models","score":0.8320000171661377},{"id":"https://openalex.org/keywords/exponential-family","display_name":"exponential family","score":0.972000002861023},{"id":"https://openalex.org/keywords/prediction-intervals","display_name":"prediction intervals","score":0.7239999771118164},{"id":"https://openalex.org/keywords/finite-sample-validity","display_name":"finite-sample validity","score":0.9139999747276306},{"id":"https://openalex.org/keywords/model-misspecification","display_name":"model misspecification","score":0.9259999990463257},{"id":"https://openalex.org/keywords/concentration-inequalities","display_name":"concentration inequalities","score":0.5009999871253967},{"id":"https://openalex.org/keywords/maximum-likelihood-estimation","display_name":"maximum likelihood estimation","score":0.9750000238418579},{"id":"https://openalex.org/keywords/probability-integral-transform","display_name":"probability integral transform","score":0.9449999928474426},{"id":"https://openalex.org/keywords/normalized-residuals","display_name":"normalized residuals","score":0.6060000061988831},{"id":"https://openalex.org/keywords/concentration-of-measure","display_name":"concentration of measure","score":0.4729999899864197},{"id":"https://openalex.org/keywords/hba1c","display_name":"HbA1c","score":0.8119999766349792},{"id":"https://openalex.org/keywords/diabetes-diagnosis","display_name":"diabetes diagnosis","score":0.5569999814033508}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7273194193840027},{"id":"https://openalex.org/C98214594","wikidata":"https://www.wikidata.org/wiki/Q850275","display_name":"Conformal map","level":2,"score":0.723213255405426},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5817143321037292},{"id":"https://openalex.org/C19493622","wikidata":"https://www.wikidata.org/wiki/Q3997735","display_name":"Probability integral transform","level":4,"score":0.5029460787773132},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4320039749145508},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4211941957473755},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.28558245301246643},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.16809648275375366},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.11511027812957764}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2944752829","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1905.03657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:2944752829","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1905.03657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"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/W2981614627","https://openalex.org/W2143860003","https://openalex.org/W3005024631","https://openalex.org/W4416736209","https://openalex.org/W2969326543","https://openalex.org/W3167215506","https://openalex.org/W4399197945","https://openalex.org/W7096880794","https://openalex.org/W4307244144","https://openalex.org/W3002272575"],"abstract_inverted_index":{"Conformal":[0],"prediction":[1,4,20,49,77,125,172,187,223,231,240],"methods":[2,183],"construct":[3],"regions":[5,21,50],"for":[6,25,161,184,189],"iid":[7],"data":[8],"that":[9,22,165],"are":[10,23,62],"valid":[11],"in":[12,235],"finite":[13,52],"samples.":[14],"We":[15,155,170],"provide":[16],"two":[17],"parametric":[18,47,75,123,203,229],"conformal":[19,48,76,124,186,204,230],"applicable":[24],"a":[26,135,157,212,245],"wide":[27],"class":[28,34],"of":[29,35,64,83,103,181,227,241],"continuous":[30,44],"statistical":[31,36],"models.":[32],"This":[33],"models":[37,41],"includes":[38],"generalized":[39],"linear":[40],"(GLMs)":[42],"with":[43],"outcomes.":[45],"Our":[46],"possesses":[51],"sample":[53],"validity,":[54,146],"even":[55],"when":[56,67,99,117],"the":[57,68,84,96,100,104,114,131,139,152,207,228],"model":[58,69],"is":[59,70,79,92,107,127,148,233],"misspecified,":[60],"and":[61,91,111,147,178,202],"asymptotically":[63,93,149],"minimal":[65,94,150],"length":[66],"correctly":[71],"specified.":[72],"The":[73,121,225],"first":[74],"region":[78,126,173,232],"constructed":[80,128],"through":[81],"binning":[82],"predictor":[85,105],"space,":[86],"guarantees":[87,143],"finite-sample":[88,144],"local":[89],"validity":[90],"at":[95,113,151],"$\\sqrt{\\log(n)/n}$":[97,153],"rate":[98,116],"dimension":[101],"$d$":[102],"space":[106],"one":[108],"or":[109],"two,":[110],"converges":[112],"$O\\{(\\log(n)/n)^{1/d}\\}$":[115],"$d":[118],">":[119],"2$.":[120],"second":[122],"by":[129],"transforming":[130],"outcome":[132],"variable":[133],"to":[134,220,238,249],"common":[136],"distribution":[137],"via":[138],"probability":[140],"integral":[141],"transform,":[142],"marginal":[145],"rate.":[154],"develop":[156],"novel":[158],"concentration":[159],"inequality":[160],"maximum":[162],"likelihood":[163],"estimation":[164],"induces":[166],"these":[167,218],"convergence":[168],"rates.":[169],"analyze":[171],"coverage":[174],"properties,":[175],"large-sample":[176],"efficiency,":[177],"robustness":[179],"properties":[180],"four":[182],"constructing":[185],"intervals":[188],"GLMs:":[190],"fully":[191],"nonparametric":[192],"kernel-based":[193],"conformal,":[194,197,201],"residual":[195,199],"based":[196,200],"normalized":[198],"which":[205],"uses":[206],"assumed":[208],"GLM":[209],"density":[210],"as":[211],"conformity":[213],"measure.":[214],"Extensive":[215],"simulations":[216],"compare":[217],"approaches":[219],"standard":[221],"asymptotic":[222],"regions.":[224],"utility":[226],"demonstrated":[234],"an":[236],"application":[237],"interval":[239],"glycosylated":[242],"hemoglobin":[243],"levels,":[244],"blood":[246],"measurement":[247],"used":[248],"diagnose":[250],"diabetes.":[251]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2019-05-16T00:00:00"}