{"id":"https://openalex.org/W4200329282","doi":"https://doi.org/10.5705/ss.202021.0048","title":"Moving Sum Data Segmentation for Stochastic Processes Based on Invariance","display_name":"Moving Sum Data Segmentation for Stochastic Processes Based on Invariance","publication_year":2021,"publication_date":"2021-10-01","ids":{"openalex":"https://openalex.org/W4200329282","doi":"https://doi.org/10.5705/ss.202021.0048"},"language":"en","primary_location":{"id":"doi:10.5705/ss.202021.0048","doi":"https://doi.org/10.5705/ss.202021.0048","is_oa":true,"landing_page_url":"https://doi.org/10.5705/ss.202021.0048","pdf_url":"https://doi.org/10.5705/ss.202021.0048","source":{"id":"https://openalex.org/S23408671","display_name":"Statistica Sinica","issn_l":"1017-0405","issn":["1017-0405","1996-8507"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","jufo-2","norway-1","poland-140","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4362561675","host_organization_name":"Institute of Statistical Science","host_organization_lineage":["https://openalex.org/P4362561675"],"host_organization_lineage_names":["Institute of Statistical Science"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistica Sinica","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.5705/ss.202021.0048","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077902725","display_name":"Claudia Kirch","orcid":"https://orcid.org/0000-0001-8135-7404","observed_orcids":["https://orcid.org/0000-0001-8135-7404"]},"institutions":[{"id":"https://openalex.org/I95793202","display_name":"Otto-von-Guericke-Universit\u00e4t Magdeburg","ror":"https://ror.org/00ggpsq73","country_code":"DE","type":"education","lineage":["https://openalex.org/I95793202"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Claudia Kirch","raw_affiliation_strings":["Otto-von-Guericke-University Magdeburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Otto-von-Guericke-University Magdeburg","institution_ids":["https://openalex.org/I95793202"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080259752","display_name":"Philipp Klein","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I95793202","display_name":"Otto-von-Guericke-Universit\u00e4t Magdeburg","ror":"https://ror.org/00ggpsq73","country_code":"DE","type":"education","lineage":["https://openalex.org/I95793202"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Philipp Klein","raw_affiliation_strings":["Otto-von-Guericke-University Magdeburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Otto-von-Guericke-University Magdeburg","institution_ids":["https://openalex.org/I95793202"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5077902725"],"corresponding_institution_ids":["https://openalex.org/I95793202"],"apc_list":null,"apc_paid":null,"fwci":0.5818,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.7241531,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9326000213623047,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9326000213623047,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.0406000018119812,"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":0.002899999963119626,"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/change-point-detection","display_name":"change point detection","score":0.8289999961853027},{"id":"https://openalex.org/keywords/multiple-change-points","display_name":"multiple change points","score":0.6579999923706055},{"id":"https://openalex.org/keywords/renewal-processes","display_name":"renewal processes","score":0.9959999918937683},{"id":"https://openalex.org/keywords/strong-invariance-principle","display_name":"strong invariance principle","score":0.7710000276565552},{"id":"https://openalex.org/keywords/asymptotic-behavior","display_name":"asymptotic behavior","score":0.5070000290870667},{"id":"https://openalex.org/keywords/consistency","display_name":"consistency","score":0.7289999723434448},{"id":"https://openalex.org/keywords/minimax-optimality","display_name":"minimax optimality","score":0.9279999732971191},{"id":"https://openalex.org/keywords/wiener-process-with-drift","display_name":"Wiener process with drift","score":0.8149999976158142},{"id":"https://openalex.org/keywords/local-change","display_name":"local change","score":0.7300000190734863},{"id":"https://openalex.org/keywords/asymptotic-distribution","display_name":"asymptotic distribution","score":0.8040000200271606}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.661501407623291},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5237890481948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5051277279853821},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.361674040555954}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5705/ss.202021.0048","doi":"https://doi.org/10.5705/ss.202021.0048","is_oa":true,"landing_page_url":"https://doi.org/10.5705/ss.202021.0048","pdf_url":"https://doi.org/10.5705/ss.202021.0048","source":{"id":"https://openalex.org/S23408671","display_name":"Statistica Sinica","issn_l":"1017-0405","issn":["1017-0405","1996-8507"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","jufo-2","norway-1","poland-140","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4362561675","host_organization_name":"Institute of Statistical Science","host_organization_lineage":["https://openalex.org/P4362561675"],"host_organization_lineage_names":["Institute of Statistical Science"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistica Sinica","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.5705/ss.202021.0048","doi":"https://doi.org/10.5705/ss.202021.0048","is_oa":true,"landing_page_url":"https://doi.org/10.5705/ss.202021.0048","pdf_url":"https://doi.org/10.5705/ss.202021.0048","source":{"id":"https://openalex.org/S23408671","display_name":"Statistica Sinica","issn_l":"1017-0405","issn":["1017-0405","1996-8507"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","jufo-2","norway-1","poland-140","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4362561675","host_organization_name":"Institute of Statistical Science","host_organization_lineage":["https://openalex.org/P4362561675"],"host_organization_lineage_names":["Institute of Statistical Science"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistica Sinica","raw_type":"journal-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3150829247","display_name":"GRK 2297: Mathematische Komplexit\u00e4tsreduktion","funder_award_id":"314838170","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G475298373","display_name":null,"funder_award_id":"GRK 2297 MathCoRe","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G796230803","display_name":null,"funder_award_id":"GRK2297","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4200329282.pdf","grobid_xml":"https://content.openalex.org/works/W4200329282.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W139040046","https://openalex.org/W188639432","https://openalex.org/W348202745","https://openalex.org/W1577286032","https://openalex.org/W1586810339","https://openalex.org/W1986651654","https://openalex.org/W1991356780","https://openalex.org/W1996850325","https://openalex.org/W1996929130","https://openalex.org/W1998701265","https://openalex.org/W2012475694","https://openalex.org/W2027726282","https://openalex.org/W2033695946","https://openalex.org/W2035279049","https://openalex.org/W2069217772","https://openalex.org/W2072157164","https://openalex.org/W2090708121","https://openalex.org/W2095153336","https://openalex.org/W2103336621","https://openalex.org/W2111369928","https://openalex.org/W2133174470","https://openalex.org/W2141319874","https://openalex.org/W2171202560","https://openalex.org/W2180399634","https://openalex.org/W2184500080","https://openalex.org/W2563678523","https://openalex.org/W2644112034","https://openalex.org/W2806846392","https://openalex.org/W2946357855","https://openalex.org/W3019220537","https://openalex.org/W3094239884","https://openalex.org/W3101925105","https://openalex.org/W3208731550"],"related_works":["https://openalex.org/W3118985732","https://openalex.org/W4392811194","https://openalex.org/W2184500080","https://openalex.org/W4290802676","https://openalex.org/W3134986835","https://openalex.org/W3040425189","https://openalex.org/W3199029374","https://openalex.org/W2955341694","https://openalex.org/W2996760694","https://openalex.org/W3208731550"],"abstract_inverted_index":{"The":[0],"segmentation":[1,35,65],"of":[2,45,66,88,109,115,127],"data":[3,34,64],"into":[4],"stationary":[5],"stretches":[6],"also":[7],"known":[8],"as":[9,23,25],"multiple":[10],"change":[11,55,80,91,129,149],"point":[12,92,130],"problem":[13],"is":[14,82],"important":[15],"for":[16,42,132,147],"many":[17],"applications":[18],"in":[19,53,56,106,117,139],"time":[20],"series":[21],"analysis":[22],"well":[24],"signal":[26],"processing.Based":[27],"on":[28],"strong":[29],"invariance":[30],"principles,":[31],"we":[32,122],"analyze":[33],"methodology":[36],"using":[37],"moving":[38],"sum":[39],"(MOSUM)":[40],"statistics":[41],"a":[43,54,107],"class":[44],"regime-switching":[46],"multivariate":[47,67],"processes":[48,74,119],"where":[49],"each":[50],"switch":[51],"results":[52],"the":[57,63,77,85,89,98,124,128,148],"drift.In":[58],"particular,":[59],"this":[60],"framework":[61],"includes":[62],"partial":[68],"sum,":[69],"integrated":[70],"diffusion":[71],"and":[72,96],"renewal":[73],"even":[75],"if":[76],"distance":[78],"between":[79],"points":[81],"sublinear.We":[83],"study":[84],"asymptotic":[86],"behavior":[87],"corresponding":[90,99],"estimators,":[93],"show":[94],"consistency":[95],"derive":[97,123,144],"localization":[100],"rates":[101],"which":[102],"are":[103],"minimax":[104],"optimal":[105],"variety":[108],"situations":[110],"including":[111],"an":[112],"unbounded":[113],"number":[114],"changes":[116,134],"Wiener":[118],"with":[120],"drift.Furthermore,":[121],"limit":[125],"distribution":[126],"estimators":[131],"local":[133],"-a":[135],"result":[136],"that":[137],"can":[138],"principle":[140],"be":[141],"used":[142],"to":[143],"confidence":[145],"intervals":[146],"points.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-12-31T00:00:00"}