{"id":"https://openalex.org/W3209957096","doi":"https://doi.org/10.1080/03610918.2021.1990328","title":"Spline estimation of partially linear regression models for time series with correlated errors","display_name":"Spline estimation of partially linear regression models for time series with correlated errors","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3209957096","doi":"https://doi.org/10.1080/03610918.2021.1990328","mag":"3209957096"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2021.1990328","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1990328","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/A5100351175","display_name":"Yan Liu","orcid":"https://orcid.org/0000-0003-4242-4840"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanping Liu","raw_affiliation_strings":["School of Economics and Statistics, Guangzhou University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Statistics, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056102926","display_name":"Juliang Yin","orcid":"https://orcid.org/0000-0001-5097-9497"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Juliang Yin","raw_affiliation_strings":["School of Economics and Statistics, Guangzhou University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Statistics, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5056102926"],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":null,"apc_paid":null,"fwci":1.1599,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.81033712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"52","issue":"11","first_page":"5522","last_page":"5536"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.992900013923645,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.992900013923645,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9900000095367432,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6765188574790955},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5855814218521118},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5712217092514038},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5555190443992615},{"id":"https://openalex.org/keywords/polynomial-regression","display_name":"Polynomial regression","score":0.5372621417045593},{"id":"https://openalex.org/keywords/smoothing-spline","display_name":"Smoothing spline","score":0.5319721102714539},{"id":"https://openalex.org/keywords/spline","display_name":"Spline (mechanical)","score":0.5248513221740723},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4943547248840332},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.4781033396720886},{"id":"https://openalex.org/keywords/multivariate-adaptive-regression-splines","display_name":"Multivariate adaptive regression splines","score":0.46776100993156433},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.4638872742652893},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.45305365324020386},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.44357335567474365},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4124511182308197},{"id":"https://openalex.org/keywords/spline-interpolation","display_name":"Spline interpolation","score":0.10371226072311401},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07347026467323303}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6765188574790955},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5855814218521118},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5712217092514038},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5555190443992615},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.5372621417045593},{"id":"https://openalex.org/C107457265","wikidata":"https://www.wikidata.org/wiki/Q7546460","display_name":"Smoothing spline","level":4,"score":0.5319721102714539},{"id":"https://openalex.org/C10390562","wikidata":"https://www.wikidata.org/wiki/Q581809","display_name":"Spline (mechanical)","level":2,"score":0.5248513221740723},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4943547248840332},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.4781033396720886},{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.46776100993156433},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.4638872742652893},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45305365324020386},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.44357335567474365},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4124511182308197},{"id":"https://openalex.org/C31447003","wikidata":"https://www.wikidata.org/wiki/Q545002","display_name":"Spline interpolation","level":3,"score":0.10371226072311401},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07347026467323303},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.0},{"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:10.1080/03610918.2021.1990328","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1990328","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":[{"id":"https://openalex.org/G7845443708","display_name":null,"funder_award_id":"61973096","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1535689967","https://openalex.org/W1570996322","https://openalex.org/W1824682467","https://openalex.org/W1976451055","https://openalex.org/W2015830357","https://openalex.org/W2022306045","https://openalex.org/W2031768652","https://openalex.org/W2036787087","https://openalex.org/W2041442087","https://openalex.org/W2042089645","https://openalex.org/W2065274576","https://openalex.org/W2090864307","https://openalex.org/W2092700154","https://openalex.org/W2126012814","https://openalex.org/W2128148355","https://openalex.org/W2131200099","https://openalex.org/W2139193919","https://openalex.org/W2142635246","https://openalex.org/W2143453991","https://openalex.org/W2168175751","https://openalex.org/W2387328794","https://openalex.org/W2491813628","https://openalex.org/W2624352981","https://openalex.org/W2766031356","https://openalex.org/W4205354563","https://openalex.org/W4235149167","https://openalex.org/W4238519965","https://openalex.org/W4292483811","https://openalex.org/W4388215482"],"related_works":["https://openalex.org/W4256152544","https://openalex.org/W1481829876","https://openalex.org/W2181828400","https://openalex.org/W3137543634","https://openalex.org/W1981039615","https://openalex.org/W4320843431","https://openalex.org/W2241490173","https://openalex.org/W2096194036","https://openalex.org/W2069371995","https://openalex.org/W2204070700"],"abstract_inverted_index":{"Partially":[0],"linear":[1,28],"regression":[2,29],"smoothing":[3],"is":[4,61,77],"a":[5,16,24],"useful":[6],"technique":[7],"for":[8,83],"modeling":[9],"time":[10,32],"series.":[11],"Using":[12],"polynomial":[13],"splines":[14],"and":[15,41],"weighted":[17],"least":[18],"squares":[19],"method,":[20],"this":[21],"study":[22],"investigates":[23],"class":[25],"of":[26,31,38,45,66,73],"partially":[27],"models":[30],"series":[33],"with":[34],"correlated":[35,68],"errors.":[36,69],"n-consistency":[37],"parametric":[39],"estimators":[40],"the":[42,46,58,71],"convergence":[43],"rate":[44],"nonparametric":[47],"estimator":[48],"are":[49],"derived":[50],"under":[51],"some":[52],"suitable":[53],"conditions.":[54],"Simulations":[55],"reveal":[56],"that":[57,65],"proposed":[59],"approach":[60],"more":[62],"valid":[63],"than":[64],"ignoring":[67],"Moreover,":[70],"importance":[72],"considering":[74],"autoregressive":[75],"errors":[76],"illustrated":[78],"by":[79],"making":[80],"multi-step-ahead":[81],"forecasts":[82],"Australian":[84],"blow-fly":[85],"data.":[86]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
