{"id":"https://openalex.org/W4366823544","doi":"https://doi.org/10.1007/s00180-023-01355-3","title":"A flexible two-piece normal dynamic linear model","display_name":"A flexible two-piece normal dynamic linear model","publication_year":2023,"publication_date":"2023-04-24","ids":{"openalex":"https://openalex.org/W4366823544","doi":"https://doi.org/10.1007/s00180-023-01355-3"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-023-01355-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-023-01355-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01355-3.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01355-3.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012834199","display_name":"Emanuele Aliverti","orcid":"https://orcid.org/0000-0002-6321-014X"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Emanuele Aliverti","raw_affiliation_strings":["Dipartimento di Scienze Statistiche, Universit\u00e0 di Padova, Padua, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Scienze Statistiche, Universit\u00e0 di Padova, Padua, Italy","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064301174","display_name":"Reinaldo B. Arellano\u2010Valle","orcid":"https://orcid.org/0000-0002-5121-9702"},"institutions":[{"id":"https://openalex.org/I162148367","display_name":"Pontificia Universidad Cat\u00f3lica de Chile","ror":"https://ror.org/04teye511","country_code":"CL","type":"education","lineage":["https://openalex.org/I162148367"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Reinaldo B. Arellano-Valle","raw_affiliation_strings":["Departamento de Estad\u00edstica, Pontificia Universidad Cat\u00f3lica de Chile, Santiago, Chile"],"affiliations":[{"raw_affiliation_string":"Departamento de Estad\u00edstica, Pontificia Universidad Cat\u00f3lica de Chile, Santiago, Chile","institution_ids":["https://openalex.org/I162148367"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078775344","display_name":"Fereshteh Kahrari","orcid":null},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fereshteh Kahrari","raw_affiliation_strings":["Dipartimento di Scienze Statistiche, Universit\u00e0 di Padova, Padua, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Scienze Statistiche, Universit\u00e0 di Padova, Padua, Italy","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075683637","display_name":"Bruno Scarpa","orcid":"https://orcid.org/0000-0002-9628-5164"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Bruno Scarpa","raw_affiliation_strings":["Dipartimento di Matematica \u201cTullio Levi Civita\u201d, Universit\u00e0 di Padova, Padua, Italy","Dipartimento di Scienze Statistiche, Universit\u00e0 di Padova, Padua, Italy","Dipartimento di Matematica \"Tullio Levi Civita\", Universit\u00e0 di Padova, Padua, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Matematica \u201cTullio Levi Civita\u201d, Universit\u00e0 di Padova, Padua, Italy","institution_ids":["https://openalex.org/I138689650"]},{"raw_affiliation_string":"Dipartimento di Scienze Statistiche, Universit\u00e0 di Padova, Padua, Italy","institution_ids":["https://openalex.org/I138689650"]},{"raw_affiliation_string":"Dipartimento di Matematica \"Tullio Levi Civita\", Universit\u00e0 di Padova, Padua, Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075683637"],"corresponding_institution_ids":["https://openalex.org/I138689650"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":0.2549,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5543102,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"38","issue":"4","first_page":"2075","last_page":"2096"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.7040146589279175},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6575686931610107},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.5695353746414185},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5326706171035767},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5116724967956543},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4924701154232025},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4772231876850128},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4708768129348755},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4534556567668915},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.44677120447158813},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43786200881004333},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4292249381542206},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.4217315912246704},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.41536587476730347},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.41162747144699097},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.29396530985832214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27130335569381714},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1926424205303192},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15095236897468567}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.7040146589279175},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6575686931610107},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.5695353746414185},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5326706171035767},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5116724967956543},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4924701154232025},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4772231876850128},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4708768129348755},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4534556567668915},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.44677120447158813},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43786200881004333},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4292249381542206},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.4217315912246704},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.41536587476730347},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41162747144699097},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.29396530985832214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27130335569381714},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1926424205303192},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15095236897468567},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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:10.1007/s00180-023-01355-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-023-01355-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01355-3.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},{"id":"pmh:oai:www.research.unipd.it:11577/3492755","is_oa":true,"landing_page_url":"https://hdl.handle.net/11577/3492755","pdf_url":"https://www.research.unipd.it/bitstream/11577/3492755/2/s00180-023-01355-3.pdf","source":{"id":"https://openalex.org/S4377196283","display_name":"Research Padua  Archive (University of Padua)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:RePEc:spr:compst:v:38:y:2023:i:4:d:10.1007_s00180-023-01355-3","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s00180-023-01355-3","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,"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":"article"}],"best_oa_location":{"id":"doi:10.1007/s00180-023-01355-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-023-01355-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01355-3.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321966","display_name":"Universit\u00e0 degli Studi di Padova","ror":"https://ror.org/00240q980"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4366823544.pdf"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W186039566","https://openalex.org/W1985037657","https://openalex.org/W2002532412","https://openalex.org/W2025634700","https://openalex.org/W2058181774","https://openalex.org/W2072585295","https://openalex.org/W2076911312","https://openalex.org/W2105934661","https://openalex.org/W2120860882","https://openalex.org/W2121448470","https://openalex.org/W2139679692","https://openalex.org/W2151147796","https://openalex.org/W2901294460","https://openalex.org/W2981116566","https://openalex.org/W3173070367","https://openalex.org/W4210616753","https://openalex.org/W4297888513"],"related_works":["https://openalex.org/W3184792886","https://openalex.org/W3146360815","https://openalex.org/W2025659129","https://openalex.org/W2390366503","https://openalex.org/W2138514533","https://openalex.org/W2089385108","https://openalex.org/W1989931226","https://openalex.org/W2098360755","https://openalex.org/W2103582908","https://openalex.org/W2278547426"],"abstract_inverted_index":{"Abstract":[0],"We":[1,27],"construct":[2],"a":[3,18,29,37,58,73,82],"flexible":[4],"dynamic":[5,75],"linear":[6,76],"model":[7,77],"for":[8,23,33],"the":[9,24,50,53],"analysis":[10],"and":[11,43,70,80],"prediction":[12],"of":[13,52],"multivariate":[14],"time":[15],"series,":[16],"assuming":[17],"two-piece":[19],"normal":[20],"initial":[21],"distribution":[22],"state":[25],"vector.":[26],"derive":[28],"novel":[30],"Kalman":[31],"filter":[32],"this":[34],"model,":[35],"obtaining":[36],"two":[38],"components":[39],"mixture":[40],"as":[41],"predictive":[42],"filtering":[44],"distributions.":[45],"In":[46],"order":[47],"to":[48,61],"estimate":[49],"covariance":[51],"error":[54],"sequences,":[55],"we":[56],"develop":[57],"Gibbs-sampling":[59],"algorithm":[60],"perform":[62],"Bayesian":[63],"inference.":[64],"The":[65],"proposed":[66],"approach":[67],"is":[68],"validated":[69],"compared":[71],"with":[72],"Gaussian":[74],"in":[78],"simulations":[79],"on":[81],"real":[83],"data":[84],"set.":[85]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
