{"id":"https://openalex.org/W7100489666","doi":null,"title":"Package hdlm: Regression Tables for High Dimensional Linear Model Estimation","display_name":"Package hdlm: Regression Tables for High Dimensional Linear Model Estimation","publication_year":null,"publication_date":null,"ids":{"openalex":"https://openalex.org/W7100489666"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.406.4499","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.406.4499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cran.at.r-project.org/web/packages/hdlm/vignettes/article.pdf","raw_type":"text"},"parent_works":[],"lineage":["https://openalex.org/W7100489666"],"child_works_count":0,"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":null,"display_name":"Taylor B. Arnold","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Taylor B. Arnold","raw_affiliation_strings":[],"raw_orcid":null,"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":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":true,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9922000169754028,"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":0.9922000169754028,"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/T13398","display_name":"Data Analysis with R","score":0.00559999980032444,"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":0.0005000000237487257,"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/high-dimensional-linear-models","display_name":"high-dimensional linear models","score":0.7699999809265137},{"id":"https://openalex.org/keywords/regression-tables","display_name":"regression tables","score":0.9549999833106995},{"id":"https://openalex.org/keywords/two-stage-selection","display_name":"two-stage selection","score":0.4309999942779541},{"id":"https://openalex.org/keywords/confidence-intervals","display_name":"confidence intervals","score":0.6380000114440918},{"id":"https://openalex.org/keywords/bayesian-hierarchical-model","display_name":"Bayesian hierarchical model","score":0.7020000219345093},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.9350000023841858},{"id":"https://openalex.org/keywords/generalized-linear-models","display_name":"generalized linear models","score":0.7919999957084656},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.699999988079071},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.7570000290870667}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5440999865531921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5267999768257141},{"id":"https://openalex.org/C32224588","wikidata":"https://www.wikidata.org/wiki/Q7250175","display_name":"Proper linear model","level":4,"score":0.4966000020503998},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4641999900341034},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.448199987411499},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40230000019073486},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.40070000290870667},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.33379998803138733},{"id":"https://openalex.org/C3019722297","wikidata":"https://www.wikidata.org/wiki/Q4440864","display_name":"High dimensional","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C176222170","wikidata":"https://www.wikidata.org/wiki/Q5157340","display_name":"Computational statistics","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.29089999198913574},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28060001134872437},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2653000056743622},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C203233044","wikidata":"https://www.wikidata.org/wiki/Q5264358","display_name":"Design matrix","level":3,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.406.4499","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.406.4499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cran.at.r-project.org/web/packages/hdlm/vignettes/article.pdf","raw_type":"text"}],"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/W7098031317","https://openalex.org/W2953097990","https://openalex.org/W7095433647","https://openalex.org/W3196705346","https://openalex.org/W7096106908","https://openalex.org/W2294146311","https://openalex.org/W4411432771","https://openalex.org/W4383218260","https://openalex.org/W3003608419"],"abstract_inverted_index":{"We":[0,37,138],"present":[1],"the":[2,9,19,42,52,58,70,80,109,110,119,180,189],"R":[3],"package":[4,60,71],"hdlm,":[5],"created":[6],"to":[7,128,133,151,187],"facilitate":[8],"study":[10],"of":[11,21,113],"high":[12,102],"dimensional":[13,82,103],"datasets.":[14],"Our":[15],"emphasis":[16],"is":[17,93],"on":[18,41,175],"production":[20],"regression":[22,104,158],"tables":[23,159],"and":[24,47,55,115,125,140,185],"a":[25,85,142,161],"class":[26],"\u2018hdlm":[27],"\u2019":[28],"for":[29,45,88,100,145],"which":[30],"new":[31],"extensions":[32],"can":[33],"be":[34,73],"easily":[35],"written.":[36],"model":[38],"our":[39],"work":[40],"functionality":[43],"given":[44,99],"linear":[46,49],"generalized":[48],"models":[50],"from":[51],"functions":[53],"lm":[54],"glm":[56],"in":[57,131,179],"recommended":[59],"stats.":[61],"Reasonable":[62],"default":[63,190],"options":[64],"have":[65],"been":[66],"selected":[67],"so":[68],"that":[69],"may":[72],"used":[74],"immediately":[75],"by":[76,122],"anyone":[77],"familiar":[78],"with":[79,118],"low":[81],"variants;":[83],"however,":[84],"generic":[86],"procedure":[87],"using":[89,160],"alternative":[90],"point":[91],"estimators":[92],"also":[94],"provided.":[95],"Two":[96],"techniques":[97],"are":[98],"constructing":[101],"tables.":[105],"The":[106,154],"first":[107],"uses":[108],"two-stage":[111],"approach":[112,164],"Wasserman":[114],"Roeder":[116],"(2009),":[117],"generalization":[120],"proposed":[121],"Meinshausen,":[123],"Meier,":[124],"B\u00fchlmann":[126],"(2009)":[127],"increase":[129],"robustness,":[130],"order":[132],"calculate":[134],"high-dimensional":[135],"p":[136,148],"values.":[137],"introduce":[139],"implement":[141],"novel":[143],"method":[144],"generalizing":[146],"these":[147],"value":[149],"methods":[150],"confidence":[152],"intervals.":[153],"second":[155],"technique":[156],"constructs":[157],"hierarchical":[162],"Bayesian":[163],"solved":[165],"via":[166],"Gibbs":[167],"Sampling":[168],"MCMC.":[169],"In":[170],"this":[171],"article,":[172],"we":[173],"focus":[174],"design":[176],"choices":[177],"made":[178],"package,":[181],"relevant":[182],"computational":[183],"issues,":[184],"approaches":[186],"changing":[188],"options.":[191]},"counts_by_year":[],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2025-10-27T00:00:00"}
