{"id":"https://openalex.org/W2593819367","doi":"https://doi.org/10.1142/s0219691317500254","title":"Minimax robust designs for wavelet estimation of nonparametric regression models with autocorrelated errors","display_name":"Minimax robust designs for wavelet estimation of nonparametric regression models with autocorrelated errors","publication_year":2017,"publication_date":"2017-03-09","ids":{"openalex":"https://openalex.org/W2593819367","doi":"https://doi.org/10.1142/s0219691317500254","mag":"2593819367"},"language":"en","primary_location":{"id":"doi:10.1142/s0219691317500254","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219691317500254","pdf_url":null,"source":{"id":"https://openalex.org/S56986848","display_name":"International Journal of Wavelets Multiresolution and Information Processing","issn_l":"0219-6913","issn":["0219-6913","1793-690X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Wavelets, Multiresolution and Information Processing","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/A5039012432","display_name":"Selvakkadunko Selvaratnam","orcid":"https://orcid.org/0000-0001-9271-1827"},"institutions":[{"id":"https://openalex.org/I130438778","display_name":"Memorial University of Newfoundland","ror":"https://ror.org/04haebc03","country_code":"CA","type":"education","lineage":["https://openalex.org/I130438778"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Selvakkadunko Selvaratnam","raw_affiliation_strings":["Department of Mathematics and Statistics, Memorial University, St. John\u2019s, NL, A1C 5S7, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Memorial University, St. John\u2019s, NL, A1C 5S7, Canada","institution_ids":["https://openalex.org/I130438778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082433582","display_name":"Alwell J. Oyet","orcid":"https://orcid.org/0000-0003-0752-6112"},"institutions":[{"id":"https://openalex.org/I130438778","display_name":"Memorial University of Newfoundland","ror":"https://ror.org/04haebc03","country_code":"CA","type":"education","lineage":["https://openalex.org/I130438778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alwell Julius Oyet","raw_affiliation_strings":["Department of Mathematics and Statistics, Memorial University, St. John\u2019s, NL, A1C 5S7, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Memorial University, St. John\u2019s, NL, A1C 5S7, Canada","institution_ids":["https://openalex.org/I130438778"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039012432"],"corresponding_institution_ids":["https://openalex.org/I130438778"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02639648,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":"03","first_page":"1750025","last_page":"1750025"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.8005819320678711},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.7359966039657593},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.6530259847640991},{"id":"https://openalex.org/keywords/ordinary-least-squares","display_name":"Ordinary least squares","score":0.6181861162185669},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5200868844985962},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.496582567691803},{"id":"https://openalex.org/keywords/generalized-least-squares","display_name":"Generalized least squares","score":0.4763084650039673},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.4433248043060303},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.44083714485168457},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.4384620785713196},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4121553301811218},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.37785786390304565},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34400731325149536},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.1474803388118744},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.11329904198646545},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.08993694186210632}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.8005819320678711},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.7359966039657593},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.6530259847640991},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.6181861162185669},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5200868844985962},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.496582567691803},{"id":"https://openalex.org/C188649462","wikidata":"https://www.wikidata.org/wiki/Q2246261","display_name":"Generalized least squares","level":3,"score":0.4763084650039673},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.4433248043060303},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.44083714485168457},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4384620785713196},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4121553301811218},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.37785786390304565},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34400731325149536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.1474803388118744},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.11329904198646545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.08993694186210632}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0219691317500254","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219691317500254","pdf_url":null,"source":{"id":"https://openalex.org/S56986848","display_name":"International Journal of Wavelets Multiresolution and Information Processing","issn_l":"0219-6913","issn":["0219-6913","1793-690X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Wavelets, Multiresolution and Information Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1498939395","https://openalex.org/W1980149518","https://openalex.org/W1982772150","https://openalex.org/W1983880232","https://openalex.org/W1996548361","https://openalex.org/W1998682508","https://openalex.org/W2003989039","https://openalex.org/W2006129770","https://openalex.org/W2024060531","https://openalex.org/W2031145085","https://openalex.org/W2032659413","https://openalex.org/W2038084354","https://openalex.org/W2039647061","https://openalex.org/W2043174953","https://openalex.org/W2043648239","https://openalex.org/W2044988778","https://openalex.org/W2065093018","https://openalex.org/W2065233607","https://openalex.org/W2067124614","https://openalex.org/W2069565844","https://openalex.org/W2100764617","https://openalex.org/W2103368439","https://openalex.org/W2111060966","https://openalex.org/W2114764995","https://openalex.org/W2159502195","https://openalex.org/W2210530496","https://openalex.org/W2321741636","https://openalex.org/W4230652951","https://openalex.org/W4230925129","https://openalex.org/W4249301842","https://openalex.org/W4255272544","https://openalex.org/W4255349969","https://openalex.org/W4295332281","https://openalex.org/W4302976813"],"related_works":["https://openalex.org/W2164006520","https://openalex.org/W1913529549","https://openalex.org/W2784244297","https://openalex.org/W4250719734","https://openalex.org/W2167126078","https://openalex.org/W1984191672","https://openalex.org/W2890271766","https://openalex.org/W2109595325","https://openalex.org/W4251541326","https://openalex.org/W1997130844"],"abstract_inverted_index":{"We":[0,43,92,157],"discuss":[1],"the":[2,16,35,46,49,70,74,78,83,87,99,103,117,127,149,160,176,188],"construction":[3],"of":[4,8,29,34,48,73,86,102,145,148],"designs":[5,96,110,162,177],"for":[6,178],"estimation":[7],"nonparametric":[9],"regression":[10],"models":[11],"with":[12,39],"autocorrelated":[13],"errors":[14],"when":[15,173,187],"mean":[17,105],"response":[18],"is":[19],"to":[20,113,131,175],"be":[21,53,114],"approximated":[22],"by":[23,55,61,69,97,125],"a":[24,143],"finite":[25],"order":[26],"linear":[27,79],"combination":[28],"dilated":[30],"and":[31,89],"translated":[32],"versions":[33],"Daubechies":[36],"wavelet":[37,75],"bases":[38],"four":[40],"vanishing":[41],"moments.":[42],"assume":[44],"that":[45,159],"parameters":[47],"resulting":[50],"model":[51],"will":[52],"estimated":[54],"weighted":[56],"least":[57,63,182],"squares":[58,64,183],"(WLS)":[59],"or":[60,151,180],"generalized":[62],"(GLS).":[65],"The":[66],"bias":[67],"induced":[68],"unused":[71],"components":[72],"bases,":[76],"in":[77,116],"approximation,":[80],"then":[81],"inflates":[82],"natural":[84],"variation":[85],"WLS":[88,179],"GLS":[90,167],"estimates.":[91],"therefore":[93],"construct":[94],"our":[95],"minimizing":[98],"maximum":[100],"value":[101],"average":[104],"squared":[106],"error":[107],"(AMSE).":[108],"Such":[109],"are":[111,123,140],"said":[112],"robust":[115],"minimax":[118],"sense.":[119],"Our":[120],"illustrative":[121],"examples":[122],"constructed":[124,164],"using":[126],"simulated":[128],"annealing":[129],"algorithm":[130],"select":[132],"an":[133],"optimal":[134],"[Formula:":[135,154,191],"see":[136,155,192],"text]-point":[137],"design,":[138],"which":[139],"integers,":[141],"from":[142],"grid":[144],"possible":[146],"values":[147],"explanatory":[150],"design":[152],"variable":[153],"text].":[156],"found":[158],"integer-valued":[161],"we":[163],"based":[165],"on":[166],"estimation,":[168,185],"have":[169],"smaller":[170],"minimum":[171],"loss":[172],"compared":[174],"ordinary":[181],"(OLS)":[184],"except":[186],"correlation":[189],"parameter":[190],"text]":[193],"approaches":[194],"1.":[195]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}