{"id":"https://openalex.org/W2283539715","doi":"https://doi.org/10.7287/peerj.preprints.90v3","title":"Closed-Form Estimation of Multiple Change-Point Models","display_name":"Closed-Form Estimation of Multiple Change-Point Models","publication_year":2013,"publication_date":"2013-12-09","ids":{"openalex":"https://openalex.org/W2283539715","doi":"https://doi.org/10.7287/peerj.preprints.90v3","mag":"2283539715"},"language":"en","primary_location":{"id":"doi:10.7287/peerj.preprints.90v3","doi":"https://doi.org/10.7287/peerj.preprints.90v3","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.90v3","pdf_url":null,"source":null,"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":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.7287/peerj.preprints.90v3","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031578230","display_name":"Greg Jensen","orcid":"https://orcid.org/0000-0001-5050-4360","observed_orcids":["https://orcid.org/0000-0001-5050-4360"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Greg Jensen","raw_affiliation_strings":["Department of Psychology, Columbia University, New York, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Psychology, Columbia University, New York, NY, United States","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5031578230"],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":13,"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.7445999979972839,"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":0.7445999979972839,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.1875,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13500","display_name":"Probability and Statistical Research","score":0.008700000122189522,"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":0.972000002861023},{"id":"https://openalex.org/keywords/multiple-change-points","display_name":"multiple change points","score":0.7699999809265137},{"id":"https://openalex.org/keywords/bayesian-model-selection","display_name":"Bayesian model selection","score":0.5460000038146973},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"marginal likelihood","score":0.8880000114440918},{"id":"https://openalex.org/keywords/binary-partitioning","display_name":"binary partitioning","score":0.953000009059906},{"id":"https://openalex.org/keywords/conjugate-priors","display_name":"conjugate priors","score":0.8849999904632568},{"id":"https://openalex.org/keywords/closed-form-estimation","display_name":"closed-form estimation","score":0.8849999904632568},{"id":"https://openalex.org/keywords/time-series-discontinuity","display_name":"time series discontinuity","score":0.7429999709129333},{"id":"https://openalex.org/keywords/multivariate-change-point-models","display_name":"multivariate change-point models","score":0.753000020980835},{"id":"https://openalex.org/keywords/psychophysics","display_name":"psychophysics","score":0.4429999887943268},{"id":"https://openalex.org/keywords/biomedical-engineering","display_name":"biomedical engineering","score":0.9190000295639038}],"concepts":[{"id":"https://openalex.org/C26004113","wikidata":"https://www.wikidata.org/wiki/Q3711784","display_name":"Conjugate prior","level":4,"score":0.727751612663269},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.6913038492202759},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.5908543467521667},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5826610326766968},{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.5712329149246216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5329049229621887},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.5101709365844727},{"id":"https://openalex.org/C33643355","wikidata":"https://www.wikidata.org/wiki/Q5176731","display_name":"Count data","level":3,"score":0.5048106908798218},{"id":"https://openalex.org/C168773036","wikidata":"https://www.wikidata.org/wiki/Q264164","display_name":"Recursion (computer science)","level":2,"score":0.47911739349365234},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4731118381023407},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4529992938041687},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.43869829177856445},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4364681839942932},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4320254921913147},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.416775107383728},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3931240439414978},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3911769986152649},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3306657671928406},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.7287/peerj.preprints.90v3","doi":"https://doi.org/10.7287/peerj.preprints.90v3","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.90v3","pdf_url":null,"source":null,"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":"posted-content"}],"best_oa_location":{"id":"doi:10.7287/peerj.preprints.90v3","doi":"https://doi.org/10.7287/peerj.preprints.90v3","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.90v3","pdf_url":null,"source":null,"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":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W151981251","https://openalex.org/W373970991","https://openalex.org/W616902621","https://openalex.org/W639907764","https://openalex.org/W1548390378","https://openalex.org/W1580438238","https://openalex.org/W1665332082","https://openalex.org/W1960862715","https://openalex.org/W1967042859","https://openalex.org/W1972972727","https://openalex.org/W1974315578","https://openalex.org/W1976041275","https://openalex.org/W1982891217","https://openalex.org/W2006681603","https://openalex.org/W2007314042","https://openalex.org/W2011631075","https://openalex.org/W2011862666","https://openalex.org/W2017146523","https://openalex.org/W2038012388","https://openalex.org/W2045656233","https://openalex.org/W2050636233","https://openalex.org/W2099948464","https://openalex.org/W2101122849","https://openalex.org/W2104128511","https://openalex.org/W2104232463","https://openalex.org/W2106706098","https://openalex.org/W2113832560","https://openalex.org/W2114337651","https://openalex.org/W2134069175","https://openalex.org/W2142499192","https://openalex.org/W2146855196","https://openalex.org/W2155755608","https://openalex.org/W2159450977","https://openalex.org/W2264468602","https://openalex.org/W2795495912","https://openalex.org/W3125212327","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4250177907","https://openalex.org/W6903709393","https://openalex.org/W1479013767","https://openalex.org/W7095345081","https://openalex.org/W2182976510","https://openalex.org/W2171304305","https://openalex.org/W3009830007","https://openalex.org/W1486660194","https://openalex.org/W2106453800","https://openalex.org/W2254666290"],"abstract_inverted_index":{"Identifying":[0],"discontinuities":[1],"(or":[2],"change-points":[3,27,92],")":[4],"in":[5,66,162],"otherwise":[6],"stationary":[7],"time":[8,45],"series":[9,46],"is":[10,47,74],"a":[11,18,35,44,63,111],"powerful":[12],"analytic":[13],"tool.":[14],"This":[15],"paper":[16],"outlines":[17],"general":[19],"strategy":[20,36],"for":[21,105],"identifying":[22],"an":[23],"unknown":[24],"number":[25],"of":[26,31,37,53,79,113],"using":[28],"elementary":[29],"principles":[30],"Bayesian":[32],"statistics.":[33],"Using":[34,98],"binary":[38],"partitioning":[39],"by":[40,143],"marginal":[41,68],"likelihood":[42],",":[43],"recursively":[48],"subdivided":[49],"on":[50],"the":[51,67,77,84,99,145],"basis":[52],"whether":[54],"adding":[55],"divisions":[56],"(and":[57],"thus":[58],"increasing":[59],"model":[60,69],"complexity)":[61],"yields":[62,83],"justified":[64],"improvement":[65],"likelihood.":[70],"When":[71],"this":[72],"approach":[73],"combined":[75],"with":[76],"use":[78],"conjugate":[80],"priors,":[81],"it":[82],"Conjugate":[85],"Partitioned":[86],"Recursion":[87],"(CPR)":[88],"algorithm,":[89,101],"which":[90,144],"identifies":[91],"without":[93],"computationally":[94],"intensive":[95],"numerical":[96],"integration.":[97],"CPR":[100,146],"methods":[102],"are":[103,153,167],"described":[104],"specifying":[106],"change-point":[107],"models":[108],"drawn":[109],"from":[110],"host":[112],"familiar":[114],"distributions,":[115],"both":[116],"discrete":[117],"(binomial,":[118],"geometric,":[119],"Poisson)":[120],"and":[121,126,138,155,164],"continuous":[122],"(exponential,":[123],"Gaussian,":[124],"uniform,":[125],"multiple":[127],"linear":[128,140],"regression),":[129],"as":[130,132],"well":[131],"multivariate":[133,136,139],"distribution":[134],"(multinomial,":[135],"normal,":[137],"regression).":[141],"Methods":[142],"algorithm":[147],"could":[148],"be":[149],"extended":[150],"or":[151],"modified":[152],"discussed,":[154],"several":[156],"detailed":[157],"applications":[158],"to":[159],"data":[160],"published":[161],"psychology":[163],"biomedical":[165],"engineering":[166],"described.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2016-06-24T00:00:00"}