{"id":"https://openalex.org/W76248335","doi":"https://doi.org/10.1007/978-3-7908-2604-3_19","title":"Empirical Dynamics and Functional Data Analysis","display_name":"Empirical Dynamics and Functional Data Analysis","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W76248335","doi":"https://doi.org/10.1007/978-3-7908-2604-3_19","mag":"76248335"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-7908-2604-3_19","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-7908-2604-3_19","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of COMPSTAT'2010","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050634491","display_name":"Hans\u2010Georg M\u00fcller","orcid":"https://orcid.org/0000-0002-6396-2552"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hans-Georg M\u00fcller","raw_affiliation_strings":["Department of Statistics, University of California, Davis One Shields Avenue, Davis, CA, 95616, U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Davis One Shields Avenue, Davis, CA, 95616, U.S.A","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5050634491"],"corresponding_institution_ids":["https://openalex.org/I84218800"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0482337,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"209","last_page":"218"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9936000108718872,"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":0.9936000108718872,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9930999875068665,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9864000082015991,"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/unobservable","display_name":"Unobservable","score":0.78441321849823},{"id":"https://openalex.org/keywords/functional-data-analysis","display_name":"Functional data analysis","score":0.6069560647010803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5656399130821228},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4959040582180023},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.45377662777900696},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4442581832408905},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41716018319129944},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40255701541900635},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3514195382595062},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34906864166259766},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.33627063035964966},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2907814085483551},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17607644200325012},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1607649028301239}],"concepts":[{"id":"https://openalex.org/C2780695315","wikidata":"https://www.wikidata.org/wiki/Q3799040","display_name":"Unobservable","level":2,"score":0.78441321849823},{"id":"https://openalex.org/C51820054","wikidata":"https://www.wikidata.org/wiki/Q5508814","display_name":"Functional data analysis","level":2,"score":0.6069560647010803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5656399130821228},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4959040582180023},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.45377662777900696},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4442581832408905},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41716018319129944},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40255701541900635},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3514195382595062},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34906864166259766},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.33627063035964966},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2907814085483551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17607644200325012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1607649028301239},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-7908-2604-3_19","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-7908-2604-3_19","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of COMPSTAT'2010","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W78182116","https://openalex.org/W206706296","https://openalex.org/W563799425","https://openalex.org/W1536425062","https://openalex.org/W1570294720","https://openalex.org/W1963713509","https://openalex.org/W1978070851","https://openalex.org/W2002815527","https://openalex.org/W2013023753","https://openalex.org/W2033527146","https://openalex.org/W2033779771","https://openalex.org/W2036705201","https://openalex.org/W2038580754","https://openalex.org/W2041655386","https://openalex.org/W2047583414","https://openalex.org/W2048868008","https://openalex.org/W2051317140","https://openalex.org/W2056592727","https://openalex.org/W2099514316","https://openalex.org/W2102284541","https://openalex.org/W2127268040","https://openalex.org/W2136261709","https://openalex.org/W2144148350","https://openalex.org/W3021971632","https://openalex.org/W3100047626","https://openalex.org/W3101275405","https://openalex.org/W3102307054","https://openalex.org/W3137136951","https://openalex.org/W4229766664","https://openalex.org/W4239442335","https://openalex.org/W4240782261"],"related_works":["https://openalex.org/W2614563012","https://openalex.org/W4293337373","https://openalex.org/W1968533609","https://openalex.org/W1984270607","https://openalex.org/W2151842462","https://openalex.org/W2349333549","https://openalex.org/W2116445905","https://openalex.org/W2486803649","https://openalex.org/W2169866437","https://openalex.org/W1964286703"],"abstract_inverted_index":{"We":[0,98],"review":[1,99],"some":[2],"recent":[3],"developments":[4],"on":[5,23,91],"modeling":[6,50],"and":[7,30,40,114,125,133],"estimation":[8,101],"of":[9,15,46,67,84,102,128],"dynamic":[10],"phenomena":[11],"within":[12],"the":[13,54,100,107,122],"framework":[14],"Functional":[16,85],"Data":[17,86],"Analysis":[18,87],"(FDA).":[19],"The":[20],"focus":[21],"is":[22,52],"longitudinal":[24],"data":[25,55],"which":[26],"correspond":[27],"to":[28,110],"sparsely":[29],"irregularly":[31],"sampled":[32],"repeated":[33],"measurements":[34,78],"that":[35,53,64,88,121,126],"are":[36,41,56,65,89],"contaminated":[37],"with":[38,74],"noise":[39],"available":[42,79],"for":[43,104],"a":[44,68,76,129,134],"sample":[45],"subjects.":[47],"A":[48],"main":[49],"assumption":[51],"generated":[57],"by":[58],"underlying":[59],"but":[60],"unobservable":[61],"smooth":[62],"trajectories":[63,94],"realizations":[66],"Gaussian":[69],"process.":[70,136],"In":[71],"this":[72],"setting,":[73],"only":[75],"few":[77],"per":[80],"subject,":[81],"classical":[82],"methods":[83],"based":[90],"presmoothing":[92],"individual":[93],"will":[95],"not":[96],"work.":[97],"derivatives":[103],"sparse":[105],"data,":[106],"PACE":[108],"package":[109],"implement":[111],"these":[112],"procedures,":[113],"an":[115],"empirically":[116],"derived":[117],"stochastic":[118],"differential":[119],"equation":[120],"processes":[123],"satisfy":[124],"consists":[127],"linear":[130],"deterministic":[131],"component":[132],"drift":[135]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}