{"id":"https://openalex.org/W4405543180","doi":"https://doi.org/10.1214/24-ejs2292","title":"High-dimensional latent Gaussian count time series: Concentration results for autocovariances and applications","display_name":"High-dimensional latent Gaussian count time series: Concentration results for autocovariances and applications","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4405543180","doi":"https://doi.org/10.1214/24-ejs2292"},"language":"en","primary_location":{"id":"doi:10.1214/24-ejs2292","doi":"https://doi.org/10.1214/24-ejs2292","is_oa":true,"landing_page_url":"https://doi.org/10.1214/24-ejs2292","pdf_url":"https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-18/issue-2/High-dimensional-latent-Gaussian-count-time-series--Concentration-results/10.1214/24-EJS2292.pdf","source":{"id":"https://openalex.org/S997959834","display_name":"Electronic Journal of Statistics","issn_l":"1935-7524","issn":["1935-7524"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","jufo-2","ki-jl-1","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Journal of Statistics","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-18/issue-2/High-dimensional-latent-Gaussian-count-time-series--Concentration-results/10.1214/24-EJS2292.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061739000","display_name":"Marie\u2010Christine D\u00fcker","orcid":"https://orcid.org/0000-0001-7065-9976","observed_orcids":["https://orcid.org/0000-0001-7065-9976"]},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]},{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["DE","US"],"is_corresponding":false,"raw_author_name":"Marie-Christine D\u00fcker","raw_affiliation_strings":["Department of Data Science, FAU Erlangen-N\u00fcrnberg; Department of Statistics and Data Science, Cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Science, FAU Erlangen-N\u00fcrnberg; Department of Statistics and Data Science, Cornell University","institution_ids":["https://openalex.org/I181369854","https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006419852","display_name":"Robert Lund","orcid":"https://orcid.org/0000-0002-2705-1583","observed_orcids":["https://orcid.org/0000-0002-2705-1583"]},"institutions":[{"id":"https://openalex.org/I185103710","display_name":"University of California, Santa Cruz","ror":"https://ror.org/03s65by71","country_code":"US","type":"education","lineage":["https://openalex.org/I185103710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Lund","raw_affiliation_strings":["Department of Statistics, University of California, Santa Cruz"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Santa Cruz","institution_ids":["https://openalex.org/I185103710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004623767","display_name":"Vladas Pipiras","orcid":"https://orcid.org/0000-0001-6838-6294","observed_orcids":["https://orcid.org/0000-0001-6838-6294"]},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vladas Pipiras","raw_affiliation_strings":["Department of Statistics and Operations Research, UNC at Chapel Hill"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Operations Research, UNC at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":675,"currency":"USD","value_usd":675},"apc_paid":{"value":675,"currency":"USD","value_usd":675},"fwci":1.3819,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83823839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"18","issue":"2","first_page":"5484","last_page":"5562"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9545999765396118,"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":0.9545999765396118,"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/T11911","display_name":"Spatial and Panel Data Analysis","score":0.009399999864399433,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13500","display_name":"Probability and Statistical Research","score":0.008799999952316284,"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/count-time-series","display_name":"count time series","score":0.902999997138977},{"id":"https://openalex.org/keywords/latent-gaussian-models","display_name":"latent Gaussian models","score":0.6669999957084656},{"id":"https://openalex.org/keywords/concentration-bounds","display_name":"concentration bounds","score":0.6919999718666077},{"id":"https://openalex.org/keywords/autocovariance-estimation","display_name":"autocovariance estimation","score":0.8629999756813049},{"id":"https://openalex.org/keywords/high-dimensional-statistics","display_name":"high-dimensional statistics","score":0.7139999866485596},{"id":"https://openalex.org/keywords/vector-autoregression","display_name":"vector autoregression","score":0.8769999742507935},{"id":"https://openalex.org/keywords/sparse-estimation","display_name":"sparse estimation","score":0.7300000190734863},{"id":"https://openalex.org/keywords/stationary-time-series","display_name":"stationary time series","score":0.546999990940094}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.848734974861145},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6893652677536011},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4969387352466583},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.48499128222465515},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.47803792357444763},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4470870792865753},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4412861764431},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.421626478433609},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4148629903793335},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0741121768951416},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1214/24-ejs2292","doi":"https://doi.org/10.1214/24-ejs2292","is_oa":true,"landing_page_url":"https://doi.org/10.1214/24-ejs2292","pdf_url":"https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-18/issue-2/High-dimensional-latent-Gaussian-count-time-series--Concentration-results/10.1214/24-EJS2292.pdf","source":{"id":"https://openalex.org/S997959834","display_name":"Electronic Journal of Statistics","issn_l":"1935-7524","issn":["1935-7524"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","jufo-2","ki-jl-1","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Journal of Statistics","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2301.00491","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2301.00491","pdf_url":"https://arxiv.org/pdf/2301.00491","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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2301.00491","doi":"https://doi.org/10.48550/arxiv.2301.00491","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2301.00491","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1214/24-ejs2292","doi":"https://doi.org/10.1214/24-ejs2292","is_oa":true,"landing_page_url":"https://doi.org/10.1214/24-ejs2292","pdf_url":"https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-18/issue-2/High-dimensional-latent-Gaussian-count-time-series--Concentration-results/10.1214/24-EJS2292.pdf","source":{"id":"https://openalex.org/S997959834","display_name":"Electronic Journal of Statistics","issn_l":"1935-7524","issn":["1935-7524"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","jufo-2","ki-jl-1","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Journal of Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2343957700","display_name":"Network Time Series: From Dynamics to Coevolution","funder_award_id":"2113662","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3285430079","display_name":"HDR TRIPODS: Collaborative Research: Foundations of Greater Data Science","funder_award_id":"1934985","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4133067109","display_name":"RTG: Networks: Foundations in Probability, Optimization, and Data Sciences","funder_award_id":"2134107","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8990798503","display_name":"On a General Class of Count Time Series Models","funder_award_id":"1407480","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405543180.pdf","grobid_xml":"https://content.openalex.org/works/W4405543180.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1495663238","https://openalex.org/W1525020851","https://openalex.org/W1913957972","https://openalex.org/W1925915230","https://openalex.org/W1977944924","https://openalex.org/W1991952585","https://openalex.org/W1994267238","https://openalex.org/W2043065155","https://openalex.org/W2044121999","https://openalex.org/W2053637529","https://openalex.org/W2067067488","https://openalex.org/W2070459503","https://openalex.org/W2095763317","https://openalex.org/W2099210013","https://openalex.org/W2150002853","https://openalex.org/W2157518455","https://openalex.org/W2396000217","https://openalex.org/W2524520044","https://openalex.org/W2612022290","https://openalex.org/W2785583235","https://openalex.org/W2790715280","https://openalex.org/W2921545086","https://openalex.org/W3137656297","https://openalex.org/W3148730259","https://openalex.org/W3175459703","https://openalex.org/W3215260960","https://openalex.org/W4241938815","https://openalex.org/W4241983685","https://openalex.org/W4390055434","https://openalex.org/W6944420810"],"related_works":["https://openalex.org/W7135104027","https://openalex.org/W2899054294","https://openalex.org/W3175459703","https://openalex.org/W3192276952","https://openalex.org/W4392736182","https://openalex.org/W7100871777","https://openalex.org/W2404827031","https://openalex.org/W7099962187","https://openalex.org/W2996540354","https://openalex.org/W4296567388"],"abstract_inverted_index":{"This":[0],"work":[1],"considers":[2],"stationary":[3,16],"vector":[4,17,47,67,139],"count":[5,118],"time":[6],"series":[7,49,119,136],"models":[8],"defined":[9],"via":[10],"deterministic":[11],"functions":[12],"of":[13,83,113],"a":[14,27,61,66,92,138,148],"latent":[15,73,88,108,134],"Gaussian":[18,48,74,89,109,135],"series.":[19,90],"The":[20,46,125],"construction":[21],"is":[22,137],"very":[23],"general":[24],"and":[25,56,86,106,120,141],"ensures":[26],"pre-specified":[28],"marginal":[29,123],"distribution":[30],"for":[31,100,115],"the":[32,53,72,81,84,87,101,104,116,121,130,133],"counts":[33,85],"in":[34,111],"each":[35],"dimension,":[36],"depending":[37],"on":[38],"unknown":[39],"parameters":[40,143],"that":[41,71],"can":[42,76],"be":[43,77],"marginally":[44],"estimated.":[45],"injects":[50],"flexibility":[51],"into":[52],"model\u2019s":[54],"temporal":[55],"cross-dimensional":[57],"dependencies,":[58],"perhaps":[59],"through":[60,147],"parametric":[62],"model":[63,75],"akin":[64],"to":[65,129],"autoregression.":[68],"We":[69],"show":[70],"estimated":[78,105,122,145],"by":[79],"relating":[80],"covariances":[82],"In":[91],"possibly":[93],"high-dimensional":[94],"setting,":[95],"concentration":[96],"bounds":[97],"are":[98,127,144],"established":[99],"differences":[102],"between":[103],"true":[107],"autocovariances,":[110],"terms":[112],"those":[114],"observed":[117],"parameters.":[124],"results":[126],"applied":[128],"case":[131],"where":[132],"autoregression,":[140],"its":[142],"sparsely":[146],"LASSO-type":[149],"procedure.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2024-12-19T00:00:00"}