{"id":"https://openalex.org/W2147264697","doi":"https://doi.org/10.1109/isit.2009.5205755","title":"Convergence rate on a nonparametric estimator for the conditional mean","display_name":"Convergence rate on a nonparametric estimator for the conditional mean","publication_year":2009,"publication_date":"2009-06-01","ids":{"openalex":"https://openalex.org/W2147264697","doi":"https://doi.org/10.1109/isit.2009.5205755","mag":"2147264697"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2009.5205755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2009.5205755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Symposium on Information Theory","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100617792","display_name":"Dong Sik Kim","orcid":"https://orcid.org/0000-0002-1001-169X"},"institutions":[{"id":"https://openalex.org/I83436808","display_name":"Hankuk University of Foreign Studies","ror":"https://ror.org/051q2m369","country_code":"KR","type":"education","lineage":["https://openalex.org/I83436808"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Dong Sik Kim","raw_affiliation_strings":["Department of Electronic Engineering, Hankuk University of Foreign Studies, Yongin, Gyunggi-do, Korea","Department of Electronic Engineering, Hankuk University of Foreign Studies, Yongin, Gyunggi-do, 449-791, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Hankuk University of Foreign Studies, Yongin, Gyunggi-do, Korea","institution_ids":["https://openalex.org/I83436808"]},{"raw_affiliation_string":"Department of Electronic Engineering, Hankuk University of Foreign Studies, Yongin, Gyunggi-do, 449-791, Korea","institution_ids":["https://openalex.org/I83436808"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100617792"],"corresponding_institution_ids":["https://openalex.org/I83436808"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.22391304,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"it 49","issue":null,"first_page":"453","last_page":"457"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9983000159263611,"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":0.9983000159263611,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9855999946594238,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9801999926567078,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.8331286907196045},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6903770565986633},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6547366976737976},{"id":"https://openalex.org/keywords/conditional-expectation","display_name":"Conditional expectation","score":0.6059964895248413},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.5308103561401367},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5149844288825989},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.4976964294910431},{"id":"https://openalex.org/keywords/conditional-variance","display_name":"Conditional variance","score":0.48145192861557007},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.4316141605377197},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.37597426772117615},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.22674351930618286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.20154300332069397},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.09368997812271118}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.8331286907196045},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6903770565986633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6547366976737976},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.6059964895248413},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.5308103561401367},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5149844288825989},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4976964294910431},{"id":"https://openalex.org/C21430997","wikidata":"https://www.wikidata.org/wiki/Q5159279","display_name":"Conditional variance","level":4,"score":0.48145192861557007},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.4316141605377197},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.37597426772117615},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.22674351930618286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.20154300332069397},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.09368997812271118},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit.2009.5205755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2009.5205755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Symposium on Information Theory","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2034562813","https://openalex.org/W2099494899","https://openalex.org/W2149534405","https://openalex.org/W2156909104","https://openalex.org/W2797333853","https://openalex.org/W2799796486","https://openalex.org/W3145397015","https://openalex.org/W3150555252","https://openalex.org/W4238717354","https://openalex.org/W4251366914","https://openalex.org/W6675129412"],"related_works":["https://openalex.org/W1541412963","https://openalex.org/W2184572292","https://openalex.org/W1509119367","https://openalex.org/W4321367829","https://openalex.org/W4309301076","https://openalex.org/W3122555292","https://openalex.org/W2041704562","https://openalex.org/W2288767749","https://openalex.org/W4313160815","https://openalex.org/W2181913714"],"abstract_inverted_index":{"The":[0],"conditional":[1,32,62,83,102],"mean":[2,13,63,66],"is":[3,47,54,78,94,105,170,176],"an":[4,41,95],"optimal":[5],"predictor":[6],"in":[7,64,187],"the":[8,12,31,37,45,57,61,65,75,79,82,87,90,98,101,110,123,126,129,134,145,149,151,154,159,163,173],"sense":[9],"of":[10,67,81,97,100,122,128,136,153],"minimizing":[11],"square":[14],"error":[15,77,132,157,160],"between":[16],"a":[17,21,34,120],"random":[18,25],"variable":[19],"and":[20,85,115,158,168],"prediction":[22],"using":[23,49],"another":[24],"variable.":[26],"In":[27,138],"order":[28,68],"to":[29,60,178],"find":[30],"mean,":[33],"nonparametric":[35],"estimator,":[36],"Nadaraya-Watson":[38,146],"estimator":[39,58,88,147,164],"with":[40,70,144,182],"indicator":[42],"function":[43],"for":[44,109,141,148],"kernel,":[46],"considered":[48],"m":[50],"sample":[51],"pairs.":[52],"It":[53,104],"known":[55,107],"that":[56,74,86,118,172],"converges":[59],"2":[69],"rate":[71,135,175],"m-4/5.":[72],"Note":[73],"minimized":[76,130,155],"expectation":[80,99],"variance":[84],"minimizes":[89],"empirical":[91,131,156],"error,":[92],"which":[93],"estimate":[96],"variance.":[103],"also":[106,185],"that,":[108],"simple":[111],"linear":[112],"regression":[113],"model":[114],"parametric":[116],"estimators":[117],"have":[119],"form":[121],"affine":[124],"function,":[125],"bias":[127],"shows":[133],"m-1.":[137,179],"this":[139,188],"paper,":[140],"discrete":[142],"distributions":[143],"predictors,":[150],"biases":[152],"induced":[161],"by":[162],"are":[165,184],"explicitly":[166],"derived,":[167],"it":[169],"shown":[171,186],"convergence":[174],"equal":[177],"Some":[180],"discussions":[181],"examples":[183],"paper.":[189]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
