{"id":"https://openalex.org/W4382365041","doi":"https://doi.org/10.1109/itw55543.2023.10161652","title":"Shrinkage Coefficient Estimation for Regularized Tyler\u2019s M\u2013Estimator: A Leave One Out Approach","display_name":"Shrinkage Coefficient Estimation for Regularized Tyler\u2019s M\u2013Estimator: A Leave One Out Approach","publication_year":2023,"publication_date":"2023-04-23","ids":{"openalex":"https://openalex.org/W4382365041","doi":"https://doi.org/10.1109/itw55543.2023.10161652"},"language":"en","primary_location":{"id":"doi:10.1109/itw55543.2023.10161652","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/itw55543.2023.10161652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Information Theory Workshop (ITW)","raw_type":"proceedings-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/A5057948105","display_name":"Karim T. Abou\u2013Moustafa","orcid":"https://orcid.org/0000-0003-4486-3804"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Karim Abou-Moustafa","raw_affiliation_strings":["Intel Corp. Chandler,AZ,85226"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intel Corp. Chandler,AZ,85226","institution_ids":["https://openalex.org/I1343180700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5057948105"],"corresponding_institution_ids":["https://openalex.org/I1343180700"],"apc_list":null,"apc_paid":null,"fwci":0.5689,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67825137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"335","last_page":"340"},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9973000288009644,"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"}},{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9933000206947327,"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/shrinkage","display_name":"Shrinkage","score":0.8025826215744019},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7511933445930481},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6251914501190186},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5903295278549194},{"id":"https://openalex.org/keywords/shrinkage-estimator","display_name":"Shrinkage estimator","score":0.5428332090377808},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.45173487067222595},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4446841776371002},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.41823089122772217},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3891504406929016},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3818433880805969},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31836408376693726},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2338867485523224},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.12149369716644287},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.10447695851325989}],"concepts":[{"id":"https://openalex.org/C180145272","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage","level":2,"score":0.8025826215744019},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7511933445930481},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6251914501190186},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5903295278549194},{"id":"https://openalex.org/C102592046","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage estimator","level":5,"score":0.5428332090377808},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.45173487067222595},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4446841776371002},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41823089122772217},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3891504406929016},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3818433880805969},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31836408376693726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2338867485523224},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.12149369716644287},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.10447695851325989},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itw55543.2023.10161652","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/itw55543.2023.10161652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Information Theory Workshop (ITW)","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":31,"referenced_works":["https://openalex.org/W1564947197","https://openalex.org/W1956130478","https://openalex.org/W1965809243","https://openalex.org/W1965928820","https://openalex.org/W1974640819","https://openalex.org/W2008330147","https://openalex.org/W2008418230","https://openalex.org/W2014509644","https://openalex.org/W2014937354","https://openalex.org/W2016130389","https://openalex.org/W2021553162","https://openalex.org/W2034646922","https://openalex.org/W2051108596","https://openalex.org/W2062125287","https://openalex.org/W2098056745","https://openalex.org/W2112081648","https://openalex.org/W2115972246","https://openalex.org/W2147116730","https://openalex.org/W2673764533","https://openalex.org/W2692680255","https://openalex.org/W2888860102","https://openalex.org/W3004366399","https://openalex.org/W3007180546","https://openalex.org/W3099609308","https://openalex.org/W3102062959","https://openalex.org/W3111439133","https://openalex.org/W4298324231","https://openalex.org/W6640783133","https://openalex.org/W6722606809","https://openalex.org/W6773815277","https://openalex.org/W6779580406"],"related_works":["https://openalex.org/W2391958761","https://openalex.org/W2765453142","https://openalex.org/W2356780078","https://openalex.org/W2356008845","https://openalex.org/W255134961","https://openalex.org/W2594721800","https://openalex.org/W2283313230","https://openalex.org/W2074164491","https://openalex.org/W2274645452","https://openalex.org/W4302281211"],"abstract_inverted_index":{"We":[0,83],"consider":[1],"the":[2,45,51,68,79,85,112],"problem":[3],"of":[4,53,89],"estimating":[5,29],"a":[6,25,36,63,75],"regularization":[7],"parameter,":[8],"or":[9],"shrinkage":[10,80,115],"coefficient":[11,81,116],"\u03b1":[12,32],"\u2208":[13],"(0,":[14],"1)":[15],"for":[16,28,67,78,114],"regularized":[17],"Tyler":[18],"M-estimators":[19],"(RTME).":[20],"In":[21],"particular,":[22],"we":[23,61],"propose":[24,62],"data-dependent":[26],"approach":[27,47,92],"an":[30],"optimal":[31],"based":[33],"on":[34,93],"maximizing":[35],"suitably":[37],"chosen":[38],"leave-one-out":[39],"cross-validated":[40],"(LOOCV)":[41],"likelihood":[42,70],"function.":[43],"Since":[44],"LOOCV":[46,69],"scales":[48],"linearly":[49],"with":[50],"number":[52],"samples":[54],"n":[55],"and":[56,87,101],"hence":[57],"is":[58,105],"computationally":[59,64],"intensive,":[60],"efficient":[65],"approximation":[66],"function":[71],"that":[72,103],"permits":[73],"selecting":[74],"near-optimal":[76],"choice":[77],"\u03b1.":[82],"demonstrate":[84],"efficiency":[86],"accuracy":[88],"our":[90],"proposed":[91],"high-dimensional":[94],"data":[95],"sampled":[96],"from":[97],"heavy-tailed":[98],"elliptical":[99],"distributions,":[100],"show":[102],"it":[104],"consistently":[106],"better":[107],"than":[108],"other":[109],"methods":[110],"in":[111],"literature":[113],"estimation.":[117]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
