{"id":"https://openalex.org/W2912743546","doi":"https://doi.org/10.1080/03610918.2019.1565586","title":"Truncation level selection in nonparametric regression using Pad\u00e9 approximation","display_name":"Truncation level selection in nonparametric regression using Pad\u00e9 approximation","publication_year":2019,"publication_date":"2019-02-05","ids":{"openalex":"https://openalex.org/W2912743546","doi":"https://doi.org/10.1080/03610918.2019.1565586","mag":"2912743546"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2019.1565586","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1565586","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/A5083004617","display_name":"Dursun Ayd\u0131n","orcid":"https://orcid.org/0000-0001-8393-1270"},"institutions":[{"id":"https://openalex.org/I79946792","display_name":"Mu\u011fla University","ror":"https://ror.org/05n2cz176","country_code":"TR","type":"education","lineage":["https://openalex.org/I79946792"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Dursun Aydin","raw_affiliation_strings":["Faculty of Science Department of Statistics, Mu\u011fla S\u0131tk\u0131 Ko\u00e7man University, Mu\u011fla, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science Department of Statistics, Mu\u011fla S\u0131tk\u0131 Ko\u00e7man University, Mu\u011fla, Turkey","institution_ids":["https://openalex.org/I79946792"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039626539","display_name":"Ersin Y\u0131lmaz","orcid":"https://orcid.org/0000-0002-9871-4700"},"institutions":[{"id":"https://openalex.org/I79946792","display_name":"Mu\u011fla University","ror":"https://ror.org/05n2cz176","country_code":"TR","type":"education","lineage":["https://openalex.org/I79946792"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Ersin Yilmaz","raw_affiliation_strings":["Faculty of Science Department of Statistics, Mu\u011fla S\u0131tk\u0131 Ko\u00e7man University, Mu\u011fla, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science Department of Statistics, Mu\u011fla S\u0131tk\u0131 Ko\u00e7man University, Mu\u011fla, Turkey","institution_ids":["https://openalex.org/I79946792"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5039626539"],"corresponding_institution_ids":["https://openalex.org/I79946792"],"apc_list":null,"apc_paid":null,"fwci":1.2988,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76588052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"50","issue":"3","first_page":"744","last_page":"763"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13487","display_name":"Statistical and numerical algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T13487","display_name":"Statistical and numerical algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9952999949455261,"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/akaike-information-criterion","display_name":"Akaike information criterion","score":0.9191185235977173},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.8896891474723816},{"id":"https://openalex.org/keywords/truncation","display_name":"Truncation (statistics)","score":0.6756736636161804},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6011141538619995},{"id":"https://openalex.org/keywords/information-criteria","display_name":"Information Criteria","score":0.5467700362205505},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5387211441993713},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.48375678062438965},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4713681936264038},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.45846450328826904},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4404709041118622},{"id":"https://openalex.org/keywords/deviance-information-criterion","display_name":"Deviance information criterion","score":0.4393397569656372},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.38746216893196106},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.35065197944641113},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.3028348982334137}],"concepts":[{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.9191185235977173},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.8896891474723816},{"id":"https://openalex.org/C106195933","wikidata":"https://www.wikidata.org/wiki/Q7847935","display_name":"Truncation (statistics)","level":2,"score":0.6756736636161804},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6011141538619995},{"id":"https://openalex.org/C2776709221","wikidata":"https://www.wikidata.org/wiki/Q6031040","display_name":"Information Criteria","level":3,"score":0.5467700362205505},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5387211441993713},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.48375678062438965},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4713681936264038},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.45846450328826904},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4404709041118622},{"id":"https://openalex.org/C17634605","wikidata":"https://www.wikidata.org/wiki/Q972431","display_name":"Deviance information criterion","level":4,"score":0.4393397569656372},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.38746216893196106},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.35065197944641113},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3028348982334137}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2019.1565586","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1565586","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":33,"referenced_works":["https://openalex.org/W141760394","https://openalex.org/W624722751","https://openalex.org/W1482537073","https://openalex.org/W1567052132","https://openalex.org/W1981076909","https://openalex.org/W2001112900","https://openalex.org/W2003767009","https://openalex.org/W2004026774","https://openalex.org/W2008229822","https://openalex.org/W2009394335","https://openalex.org/W2015071882","https://openalex.org/W2027106381","https://openalex.org/W2044940771","https://openalex.org/W2064980127","https://openalex.org/W2065540158","https://openalex.org/W2072013944","https://openalex.org/W2096194036","https://openalex.org/W2109785413","https://openalex.org/W2114268158","https://openalex.org/W2114275657","https://openalex.org/W2133944729","https://openalex.org/W2136932198","https://openalex.org/W2162430620","https://openalex.org/W2168175751","https://openalex.org/W2171033594","https://openalex.org/W2259472751","https://openalex.org/W2477106103","https://openalex.org/W3000332379","https://openalex.org/W3108043981","https://openalex.org/W4206428854","https://openalex.org/W4212883601","https://openalex.org/W4302599714","https://openalex.org/W4312258136"],"related_works":["https://openalex.org/W4385945919","https://openalex.org/W1968886786","https://openalex.org/W2748798568","https://openalex.org/W3111001581","https://openalex.org/W2088726129","https://openalex.org/W2350396435","https://openalex.org/W2105284594","https://openalex.org/W2260619510","https://openalex.org/W2029108025","https://openalex.org/W2643715342"],"abstract_inverted_index":{"This":[0,16],"paper":[1],"introduces":[2],"a":[3,12,21,50,58,124],"Pad\u00e9-type":[4,51],"approximation":[5,19],"for":[6,67],"an":[7,63],"unknown":[8],"regression":[9,14],"function":[10],"in":[11,28,134],"nonparametric":[13],"model.":[15],"newly":[17],"introduced":[18],"provides":[20],"linear":[22],"model":[23],"with":[24,34],"multi-collinearities":[25],"and":[26,99,123,143,156],"errors":[27],"all":[29],"its":[30],"variables.":[31],"To":[32,61],"deal":[33],"these":[35,115],"issues,":[36],"we":[37,70],"used":[38],"the":[39,72,78,86,112,132,135,141,147],"truncated":[40],"total":[41],"least":[42],"squares":[43],"(TTLS)":[44],"method.":[45],"The":[46,103,137],"efficient":[47],"implementation":[48],"of":[49,85,106,114],"method":[52],"using":[53],"TTLS":[54],"depends":[55],"on":[56],"choosing":[57],"truncation":[59,65],"level.":[60],"provide":[62],"optimum":[64],"level":[66,116],"this":[68,107],"method,":[69],"update":[71],"conventional":[73],"parameter":[74],"selection":[75,117],"methods,":[76,149],"including":[77],"generalized":[79],"cross":[80],"validation":[81],"(GCV),":[82],"improved":[83],"version":[84],"Akaike":[87],"information":[88,96],"criterion":[89,97],"(AICc),":[90],"restricted":[91],"maximum":[92],"likelihood":[93],"(REML),":[94],"Bayesian":[95],"(BIC),":[98],"Mallows\u2019":[100],"Cp":[101],"criterion.":[102],"primary":[104],"aim":[105],"study":[108],"is":[109],"to":[110,130],"compare":[111],"performances":[113],"methods.":[118],"A":[119],"Monte":[120],"Carlo":[121],"simulation":[122],"real":[125],"data":[126],"example":[127],"are":[128,154],"performed":[129],"illustrate":[131],"ideas":[133],"paper.":[136],"results":[138],"confirm":[139],"that":[140],"GCV":[142],"AICc":[144],"slightly":[145],"outperform":[146],"other":[148],"especially":[150],"when":[151],"sample":[152],"sizes":[153],"small":[155],"large,":[157],"respectively.":[158]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}