{"id":"https://openalex.org/W2602255751","doi":"https://doi.org/10.1080/03610918.2017.1307395","title":"Feasible algorithm for linear mixed model for massive data","display_name":"Feasible algorithm for linear mixed model for massive data","publication_year":2017,"publication_date":"2017-03-22","ids":{"openalex":"https://openalex.org/W2602255751","doi":"https://doi.org/10.1080/03610918.2017.1307395","mag":"2602255751"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2017.1307395","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2017.1307395","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","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/A5009440841","display_name":"Yanyan Zhao","orcid":"https://orcid.org/0000-0001-7446-7330"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanyan Zhao","raw_affiliation_strings":["Institute of Statistics, Nankai University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Statistics, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5009440841"],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":0.1994,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60575873,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"47","issue":"4","first_page":"1126","last_page":"1133"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9837999939918518,"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":0.9837999939918518,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9797000288963318,"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9603000283241272,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6834898591041565},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6681758761405945},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6552916765213013},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.6086283326148987},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5713100433349609},{"id":"https://openalex.org/keywords/generalized-linear-mixed-model","display_name":"Generalized linear mixed model","score":0.49719884991645813},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.48194193840026855},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4795405864715576},{"id":"https://openalex.org/keywords/mixed-model","display_name":"Mixed model","score":0.4754321575164795},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.46310847997665405},{"id":"https://openalex.org/keywords/design-matrix","display_name":"Design matrix","score":0.43142277002334595},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.37258702516555786},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2451658546924591},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.07390323281288147}],"concepts":[{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6834898591041565},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6681758761405945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6552916765213013},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.6086283326148987},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5713100433349609},{"id":"https://openalex.org/C153720581","wikidata":"https://www.wikidata.org/wiki/Q5532490","display_name":"Generalized linear mixed model","level":2,"score":0.49719884991645813},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.48194193840026855},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4795405864715576},{"id":"https://openalex.org/C16012445","wikidata":"https://www.wikidata.org/wiki/Q1501135","display_name":"Mixed model","level":2,"score":0.4754321575164795},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.46310847997665405},{"id":"https://openalex.org/C203233044","wikidata":"https://www.wikidata.org/wiki/Q5264358","display_name":"Design matrix","level":3,"score":0.43142277002334595},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37258702516555786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2451658546924591},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.07390323281288147},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2017.1307395","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2017.1307395","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W199271301","https://openalex.org/W1562327279","https://openalex.org/W1580960713","https://openalex.org/W1920170482","https://openalex.org/W1980911127","https://openalex.org/W2013501249","https://openalex.org/W2028213769","https://openalex.org/W2033812560","https://openalex.org/W2079927753","https://openalex.org/W2114060717","https://openalex.org/W2127684760","https://openalex.org/W2133140216","https://openalex.org/W2140839682","https://openalex.org/W2146774335","https://openalex.org/W2166436449","https://openalex.org/W2291895482","https://openalex.org/W2320865122","https://openalex.org/W2333836441","https://openalex.org/W2963126228","https://openalex.org/W3106344352"],"related_works":["https://openalex.org/W4293051593","https://openalex.org/W2356286374","https://openalex.org/W2477652392","https://openalex.org/W2352749079","https://openalex.org/W218554686","https://openalex.org/W180272529","https://openalex.org/W4229377912","https://openalex.org/W3141065986","https://openalex.org/W2034690693","https://openalex.org/W2346118442"],"abstract_inverted_index":{"This":[0,37],"article":[1],"studies":[2,68],"computation":[3],"problem":[4],"in":[5],"the":[6,21,40,44,57,74,93,97],"context":[7],"of":[8,11,43,76],"estimating":[9,90],"parameters":[10],"linear":[12,25,77],"mixed":[13,26,78],"model":[14,27,79],"for":[15,81],"massive":[16,82],"data.":[17,99],"Our":[18,66],"algorithms":[19,72],"combine":[20],"factored":[22],"spectrally":[23],"transformed":[24],"method":[28,45,94],"with":[29,92],"a":[30,60],"sequential":[31],"singular":[32],"value":[33],"decomposition":[34],"calculation":[35,75],"algorithm.":[36],"combination":[38],"solves":[39],"operation":[41],"limitation":[42],"and":[46,62,87],"also":[47],"makes":[48],"this":[49],"algorithm":[50],"feasible":[51,80],"to":[52],"big":[53],"dataset,":[54],"especially":[55],"when":[56],"data":[58,83],"has":[59],"tall":[61],"thin":[63],"design":[64],"matrix.":[65],"simulation":[67],"show":[69],"that":[70],"our":[71],"make":[73],"on":[84,96],"ordinary":[85],"desktop":[86],"have":[88],"same":[89],"accuracy":[91],"based":[95],"whole":[98]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}