{"id":"https://openalex.org/W201192877","doi":null,"title":"Stochastic Dynamic Models for Functional Data","display_name":"Stochastic Dynamic Models for Functional Data","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W201192877","mag":"201192877"},"language":"en","primary_location":{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/78804","doi":null,"is_oa":true,"landing_page_url":"http://deepblue.lib.umich.edu/bitstream/2027.42/78804/1/bzhu_1.pdf","pdf_url":"https://deepblue.lib.umich.edu/bitstreams/154329ce-6000-4023-8e13-ecef4745dcc4/download","source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"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":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://deepblue.lib.umich.edu/bitstreams/154329ce-6000-4023-8e13-ecef4745dcc4/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076808228","display_name":"Bin Zhu","orcid":"https://orcid.org/0000-0003-0172-5516","observed_orcids":["https://orcid.org/0000-0003-0172-5516"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bin Zhu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-0172-5516","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5076808228"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.48510000109672546,"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"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.48510000109672546,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.26420000195503235,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.029100000858306885,"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/stochastic-dynamic-model","display_name":"stochastic dynamic model","score":0.8960000276565552},{"id":"https://openalex.org/keywords/functional-data","display_name":"functional data","score":0.878000020980835},{"id":"https://openalex.org/keywords/gaussian-processes","display_name":"Gaussian processes","score":0.718999981880188},{"id":"https://openalex.org/keywords/prostate-specific-antigen","display_name":"prostate-specific antigen","score":0.7360000014305115},{"id":"https://openalex.org/keywords/forecasting","display_name":"forecasting","score":0.6570000052452087},{"id":"https://openalex.org/keywords/covariate-effects","display_name":"covariate effects","score":0.875},{"id":"https://openalex.org/keywords/change-point-detection","display_name":"change point detection","score":0.6869999766349792},{"id":"https://openalex.org/keywords/array-based-comparative-genomic-hybridization","display_name":"array comparative genomic hybridization","score":0.9890000224113464},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6000000238418579},{"id":"https://openalex.org/keywords/euler-approximation","display_name":"Euler approximation","score":0.8650000095367432},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.9850000143051147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47195354104042053},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3333391547203064}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/78804","doi":null,"is_oa":true,"landing_page_url":"http://deepblue.lib.umich.edu/bitstream/2027.42/78804/1/bzhu_1.pdf","pdf_url":"https://deepblue.lib.umich.edu/bitstreams/154329ce-6000-4023-8e13-ecef4745dcc4/download","source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"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":"Thesis"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.966.5393","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.966.5393","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://deepblue.lib.umich.edu/bitstream/handle/2027.42/78804/bzhu_1.pdf%3Bjsessionid%3D552A60A93EA6BA3CEF8A976044494B75?sequence%3D1","raw_type":"text"},{"id":"mag:201192877","doi":null,"is_oa":false,"landing_page_url":"http://deepblue.lib.umich.edu/handle/2027.42/78804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/78804","doi":null,"is_oa":true,"landing_page_url":"http://deepblue.lib.umich.edu/bitstream/2027.42/78804/1/bzhu_1.pdf","pdf_url":"https://deepblue.lib.umich.edu/bitstreams/154329ce-6000-4023-8e13-ecef4745dcc4/download","source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"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":"Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W201192877.pdf","grobid_xml":"https://content.openalex.org/works/W201192877.grobid-xml"},"referenced_works_count":99,"referenced_works":["https://openalex.org/W41222772","https://openalex.org/W61851564","https://openalex.org/W161217653","https://openalex.org/W187557927","https://openalex.org/W191167826","https://openalex.org/W206706296","https://openalex.org/W562826997","https://openalex.org/W1484187172","https://openalex.org/W1539462193","https://openalex.org/W1575104486","https://openalex.org/W1575978816","https://openalex.org/W1597926580","https://openalex.org/W1746819321","https://openalex.org/W1974107857","https://openalex.org/W1976625337","https://openalex.org/W1977825281","https://openalex.org/W1980911127","https://openalex.org/W1983781571","https://openalex.org/W1989466762","https://openalex.org/W1994482727","https://openalex.org/W1994823593","https://openalex.org/W1996682654","https://openalex.org/W1997063559","https://openalex.org/W1999975842","https://openalex.org/W2002355073","https://openalex.org/W2005028383","https://openalex.org/W2005819708","https://openalex.org/W2015544818","https://openalex.org/W2016299037","https://openalex.org/W2017535462","https://openalex.org/W2017957151","https://openalex.org/W2022798446","https://openalex.org/W2023581626","https://openalex.org/W2030118009","https://openalex.org/W2033192403","https://openalex.org/W2035228196","https://openalex.org/W2037426238","https://openalex.org/W2042534360","https://openalex.org/W2055872736","https://openalex.org/W2056902303","https://openalex.org/W2057764457","https://openalex.org/W2059963348","https://openalex.org/W2060467039","https://openalex.org/W2065266611","https://openalex.org/W2065540158","https://openalex.org/W2066721762","https://openalex.org/W2077701646","https://openalex.org/W2080920780","https://openalex.org/W2083875149","https://openalex.org/W2085514238","https://openalex.org/W2088060415","https://openalex.org/W2088413985","https://openalex.org/W2098754649","https://openalex.org/W2100725331","https://openalex.org/W2102527604","https://openalex.org/W2104842737","https://openalex.org/W2106060143","https://openalex.org/W2106575405","https://openalex.org/W2107318095","https://openalex.org/W2110359348","https://openalex.org/W2113574907","https://openalex.org/W2115759102","https://openalex.org/W2116113455","https://openalex.org/W2119786508","https://openalex.org/W2121106110","https://openalex.org/W2122512809","https://openalex.org/W2127710810","https://openalex.org/W2130416410","https://openalex.org/W2130715829","https://openalex.org/W2131057697","https://openalex.org/W2133174470","https://openalex.org/W2134012290","https://openalex.org/W2134672574","https://openalex.org/W2138248800","https://openalex.org/W2139226739","https://openalex.org/W2143792696","https://openalex.org/W2145816995","https://openalex.org/W2147269409","https://openalex.org/W2148080284","https://openalex.org/W2151926297","https://openalex.org/W2152977846","https://openalex.org/W2153287720","https://openalex.org/W2153675946","https://openalex.org/W2154354000","https://openalex.org/W2156505355","https://openalex.org/W2158714606","https://openalex.org/W2159088369","https://openalex.org/W2168110243","https://openalex.org/W2169043705","https://openalex.org/W2170083807","https://openalex.org/W2170828133","https://openalex.org/W2317997851","https://openalex.org/W2326666389","https://openalex.org/W2332036560","https://openalex.org/W2332250001","https://openalex.org/W2525046407","https://openalex.org/W2796293253","https://openalex.org/W3021971632","https://openalex.org/W3127325877"],"related_works":["https://openalex.org/W2100634452","https://openalex.org/W76248335","https://openalex.org/W2092311839","https://openalex.org/W1993727156","https://openalex.org/W2532022359","https://openalex.org/W4381551040","https://openalex.org/W4405617577","https://openalex.org/W3033350379","https://openalex.org/W4415024446","https://openalex.org/W2286048038"],"abstract_inverted_index":{"Functional":[0],"data":[1,160,198,213,218],"arise":[2],"frequently":[3],"in":[4,184],"many":[5],"fields":[6],"of":[7,60,77,145,165],"biomedical":[8],"research":[9],"as":[10,38,54],"sequential":[11],"observations":[12,16,34],"over":[13,90],"time.":[14],"The":[15,121,187,200],"are":[17,126,204],"generated":[18],"by":[19],"an":[20,28],"unknown":[21,52],"dynamic":[22,25,67],"mechanism.":[23],"This":[24,70],"process":[26,85,139],"has":[27],"unspecified":[29],"mean":[30,42],"function,":[31],"and":[32,86,99,101,124,147,161,202,212,216],"the":[33,78,83,94,142,155,163,168,182,185],"can":[35,180],"be":[36],"considered":[37],"arising":[39],"from":[40],"this":[41,47,51],"function":[43,53,115,144],"plus":[44],"noise.":[45],"In":[46],"dissertation,":[48],"we":[49,135],"treat":[50],"a":[55,61,65,107,113,137,174],"realization":[56],"or":[57],"sample":[58],"path":[59],"stochastic":[62,66,84,176],"process,":[63,80],"using":[64,206],"model":[68,141,189],"(SDM).":[69],"will":[71],"enable":[72],"us":[73],"to":[74,111,140,157,192],"study":[75],"dynamics":[76],"underlying":[79],"including":[81],"how":[82],"its":[87],"derivatives":[88],"evolve":[89],"time,":[91],"both":[92],"within":[93],"observation":[95],"time":[96,117],"(through":[97],"estimation":[98,201],"inference)":[100],"afterwards(through":[102],"forecasting).":[103],"We":[104,152,171],"first":[105],"introduce":[106],"new":[108],"modeling":[109],"strategy":[110],"estimate":[112],"smooth":[114],"for":[116],"series":[118],"functional":[119,159],"data.":[120],"proposed":[122],"models":[123,156],"methods":[125,223],"illustrated":[127],"on":[128,167],"prostate":[129],"specific":[130],"antigen":[131],"(PSA)":[132],"data,":[133],"where":[134],"use":[136],"Gaussian":[138],"rate":[143,169],"PSA":[146],"achieve":[148],"more":[149],"precise":[150],"forecasting.":[151],"then":[153],"extend":[154],"multi-subject":[158],"consider":[162],"effect":[164],"covariates":[166],"functions.":[170],"finally":[172],"propose":[173],"time-varying":[175],"position":[177],"model,":[178],"which":[179],"approximate":[181],"breakpoints":[183],"function.":[186],"discretized":[188],"is":[190],"applied":[191],"array":[193],"comparative":[194],"genomic":[195],"hybridization":[196],"(CGH)":[197],"analysis.":[199],"inference":[203],"conducted":[205],"MCMC":[207],"algorithms":[208],"with":[209],"Euler":[210],"approximation":[211],"augmentation.":[214],"Simulations":[215],"real":[217],"analysis":[219],"demonstrate":[220],"that":[221],"our":[222],"outperform":[224],"several":[225],"alternative":[226],"approaches.":[227]},"counts_by_year":[],"updated_date":"2026-10-09T09:14:12.587421","created_date":"2016-06-24T00:00:00"}
