{"id":"https://openalex.org/W3160415809","doi":null,"title":"Non-parametric regression models for compositional data","display_name":"Non-parametric regression models for compositional data","publication_year":2020,"publication_date":"2020-02-12","ids":{"openalex":"https://openalex.org/W3160415809","mag":"3160415809"},"language":"en","primary_location":{"id":"mag:3160415809","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2002.05137.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2002.05137.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018498598","display_name":"Michail Tsagris","orcid":"https://orcid.org/0000-0002-2049-3063","observed_orcids":["https://orcid.org/0000-0002-2049-3063"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michail Tsagris","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2049-3063","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061647981","display_name":"Abdulaziz Alenazi","orcid":"https://orcid.org/0000-0002-4758-8871","observed_orcids":["https://orcid.org/0000-0002-4758-8871"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdulaziz Alenazi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4758-8871","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066860255","display_name":"Connie Stewart","orcid":"https://orcid.org/0000-0003-1058-5922","observed_orcids":["https://orcid.org/0000-0003-1058-5922"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Connie Stewart","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1058-5922","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.15189999341964722,"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":0.15189999341964722,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.12030000239610672,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.08160000294446945,"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/compositional-data","display_name":"compositional data","score":0.9900000095367432},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"nonparametric regression","score":0.9760000109672546},{"id":"https://openalex.org/keywords/k-nearest-neighbor-regression","display_name":"k-nearest neighbors regression","score":0.777999997138977},{"id":"https://openalex.org/keywords/nadaraya-watson-estimator","display_name":"Nadaraya-Watson estimator","score":0.9929999709129333},{"id":"https://openalex.org/keywords/zero-values","display_name":"zero values","score":0.7390000224113464},{"id":"https://openalex.org/keywords/big-data","display_name":"big data","score":0.6119999885559082},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.8109999895095825}],"concepts":[{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.6317988634109497},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5144439339637756},{"id":"https://openalex.org/C57381214","wikidata":"https://www.wikidata.org/wiki/Q55631393","display_name":"Regression diagnostic","level":4,"score":0.5010921955108643},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.49482959508895874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4927673637866974},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4612376391887665},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.4413045346736908},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.4385543465614319},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.42623624205589294},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4252229332923889},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.41554537415504456},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36443114280700684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32910406589508057},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32865357398986816},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3183295428752899},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.26072168350219727},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3160415809","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2002.05137.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3160415809","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2002.05137.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W187352296","https://openalex.org/W1503352355","https://openalex.org/W1522547150","https://openalex.org/W1525516619","https://openalex.org/W1565234293","https://openalex.org/W1598920400","https://openalex.org/W1929665029","https://openalex.org/W1968596810","https://openalex.org/W1969909755","https://openalex.org/W1984275135","https://openalex.org/W1988590143","https://openalex.org/W1990942470","https://openalex.org/W1998120841","https://openalex.org/W2005985894","https://openalex.org/W2009520643","https://openalex.org/W2014149589","https://openalex.org/W2022080111","https://openalex.org/W2024254343","https://openalex.org/W2024350849","https://openalex.org/W2029518234","https://openalex.org/W2029607409","https://openalex.org/W2047081748","https://openalex.org/W2047618536","https://openalex.org/W2048127846","https://openalex.org/W2049106209","https://openalex.org/W2059885445","https://openalex.org/W2061309172","https://openalex.org/W2061643264","https://openalex.org/W2063408601","https://openalex.org/W2073404504","https://openalex.org/W2078112764","https://openalex.org/W2087124656","https://openalex.org/W2091886411","https://openalex.org/W2105732805","https://openalex.org/W2136734583","https://openalex.org/W2146932984","https://openalex.org/W2159906399","https://openalex.org/W2163542501","https://openalex.org/W2169951582","https://openalex.org/W2186488806","https://openalex.org/W2211925278","https://openalex.org/W2325379241","https://openalex.org/W2398800349","https://openalex.org/W2491991542","https://openalex.org/W2603339130","https://openalex.org/W2613514963","https://openalex.org/W2622253977","https://openalex.org/W2788189914","https://openalex.org/W2889274797","https://openalex.org/W2899039999","https://openalex.org/W2904250115","https://openalex.org/W2911964244","https://openalex.org/W2934806642","https://openalex.org/W2962733802","https://openalex.org/W2963004295","https://openalex.org/W2964000908","https://openalex.org/W3045513428","https://openalex.org/W3121883049","https://openalex.org/W3122632052","https://openalex.org/W3123608026"],"related_works":["https://openalex.org/W4287868383","https://openalex.org/W4385147727","https://openalex.org/W3005805791","https://openalex.org/W4415270757","https://openalex.org/W2163542501","https://openalex.org/W7057923668","https://openalex.org/W7049275269","https://openalex.org/W7171830289","https://openalex.org/W7090764042","https://openalex.org/W1929665029"],"abstract_inverted_index":{"Compositional":[0],"data":[1,16,101,118],"arise":[2],"in":[3,17,80,157],"many":[4,66],"real-life":[5,100],"applications":[6],"and":[7,85,99,119,127],"versatile":[8],"methods":[9],"for":[10,48,72,111,171],"properly":[11],"analyzing":[12],"this":[13],"type":[14],"of":[15,27,67,106],"the":[18,28,31,57,62,68,91,104,115,149,154],"regression":[19,34,46,59,70,139],"context":[20],"are":[21,82],"needed.":[22],"This":[23],"paper,":[24],"through":[25],"use":[26,172],"$\\alpha$-transformation,":[29],"extends":[30],"classical":[32],"$k$-$NN$":[33],"to":[35,56,132,137,159],"what":[36],"is":[37,53],"termed":[38],"$\\alpha$-$k$-$NN$":[39,52,126,155],"regression,":[40,156,161],"yielding":[41],"a":[42,163],"highly":[43,169],"flexible":[44],"non-parametric":[45,109],"model":[47],"compositional":[49,73,116],"data.":[50,179],"The":[51],"further":[54],"extended":[55],"$\\alpha$-kernel":[58,128,160],"by":[60],"adopting":[61],"Nadaray-Watson":[63],"estimator.":[64],"Unlike":[65],"recommended":[69],"models":[71,93,140],"data,":[74],"zeros":[75],"values":[76],"(which":[77],"commonly":[78],"occur":[79],"practice)":[81],"not":[83],"problematic":[84],"they":[86],"can":[87,130],"be":[88],"incorporated":[89],"into":[90],"proposed":[92],"without":[94],"modification.":[95],"Extensive":[96],"simulation":[97],"studies":[98],"analyses":[102],"highlight":[103],"advantage":[105],"using":[107],"these":[108],"regressions":[110,129],"complex":[112],"relationships":[113],"between":[114],"response":[117],"Euclidean":[120],"predictor":[121,150],"variables.":[122,151],"Both":[123],"suggest":[124],"that":[125],"lead":[131],"more":[133],"accurate":[134],"predictions":[135],"compared":[136],"current":[138],"which":[141],"assume":[142],"a,":[143],"sometimes":[144],"restrictive,":[145],"parametric":[146],"relationship":[147],"with":[148,173],"In":[152],"addition,":[153],"contrast":[158],"enjoys":[162],"high":[164],"computational":[165],"efficiency":[166],"rendering":[167],"it":[168],"attractive":[170],"large":[174],"scale,":[175],"massive,":[176],"or":[177],"big":[178]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-09-26T08:19:03.415552","created_date":"2021-05-24T00:00:00"}
