{"id":"https://openalex.org/W4289655297","doi":"https://doi.org/10.1109/isit50566.2022.9834566","title":"A Data-driven Missing Mass Estimation Framework","display_name":"A Data-driven Missing Mass Estimation Framework","publication_year":2022,"publication_date":"2022-06-26","ids":{"openalex":"https://openalex.org/W4289655297","doi":"https://doi.org/10.1109/isit50566.2022.9834566"},"language":"en","primary_location":{"id":"doi:10.1109/isit50566.2022.9834566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit50566.2022.9834566","pdf_url":null,"source":{"id":"https://openalex.org/S4363604560","display_name":"2022 IEEE International Symposium on Information Theory (ISIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Symposium on Information Theory (ISIT)","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/A5012874444","display_name":"Amichai Painsky","orcid":"https://orcid.org/0000-0002-5899-5608"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":true,"raw_author_name":"Amichai Painsky","raw_affiliation_strings":["Tel Aviv University,Tel Aviv,Israel","Tel Aviv University, Tel Aviv, Israel"],"affiliations":[{"raw_affiliation_string":"Tel Aviv University,Tel Aviv,Israel","institution_ids":["https://openalex.org/I16391192"]},{"raw_affiliation_string":"Tel Aviv University, Tel Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5012874444"],"corresponding_institution_ids":["https://openalex.org/I16391192"],"apc_list":null,"apc_paid":null,"fwci":0.3118,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.49692954,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2991","last_page":"2995"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9951000213623047,"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"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9951000213623047,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9891999959945679,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9801999926567078,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/missing-data","display_name":"Missing data","score":0.7080603837966919},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5586413741111755},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5235283374786377},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48400065302848816},{"id":"https://openalex.org/keywords/countable-set","display_name":"Countable set","score":0.4807593524456024},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.41966211795806885},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.41358262300491333},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.397106796503067},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14408639073371887},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.08668404817581177}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7080603837966919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5586413741111755},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5235283374786377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48400065302848816},{"id":"https://openalex.org/C110729354","wikidata":"https://www.wikidata.org/wiki/Q185478","display_name":"Countable set","level":2,"score":0.4807593524456024},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.41966211795806885},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.41358262300491333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.397106796503067},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14408639073371887},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.08668404817581177},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit50566.2022.9834566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit50566.2022.9834566","pdf_url":null,"source":{"id":"https://openalex.org/S4363604560","display_name":"2022 IEEE International Symposium on Information Theory (ISIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6000000238418579}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322252","display_name":"Israel Science Foundation","ror":"https://ror.org/04sazxf24"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1585701772","https://openalex.org/W1638306878","https://openalex.org/W1714333848","https://openalex.org/W1836729566","https://openalex.org/W1966919650","https://openalex.org/W2017856804","https://openalex.org/W2069241007","https://openalex.org/W2074154707","https://openalex.org/W2082092506","https://openalex.org/W2088696557","https://openalex.org/W2089171488","https://openalex.org/W2116601594","https://openalex.org/W2118829919","https://openalex.org/W2146368895","https://openalex.org/W2187207766","https://openalex.org/W2400725243","https://openalex.org/W2744914785","https://openalex.org/W2886017168","https://openalex.org/W2951274389","https://openalex.org/W2963076535","https://openalex.org/W2963224888","https://openalex.org/W2963458640","https://openalex.org/W2963931291","https://openalex.org/W2979248895","https://openalex.org/W3099818804","https://openalex.org/W3099924592","https://openalex.org/W3104209297","https://openalex.org/W3174504784","https://openalex.org/W3216094510","https://openalex.org/W4200103159","https://openalex.org/W4234628562","https://openalex.org/W6635170618","https://openalex.org/W6636883245","https://openalex.org/W6686941415","https://openalex.org/W6713310226"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W4285201053","https://openalex.org/W2753779043","https://openalex.org/W4313546598"],"abstract_inverted_index":{"Consider":[0],"a":[1,9,32,123,134],"finite":[2],"sample":[3],"from":[4,138],"an":[5],"unknown":[6],"distribution":[7],"over":[8,147],"countable":[10],"alphabet.":[11],"The":[12],"missing":[13,63],"mass":[14,29,64],"refers":[15],"to":[16,45,93,127,158],"the":[17,26,46,52,70,81,89,97,139,143],"probability":[18],"of":[19,49,57,116,136],"symbols":[20],"that":[21,72,107,148],"do":[22],"not":[23],"appear":[24],"in":[25,35,69,113],"sample.":[27],"Missing":[28],"estimation":[30,65,125],"is":[31,91],"fundamental":[33],"problem":[34],"statistics,":[36],"information":[37],"theory":[38],"and":[39,51,59,141],"related":[40],"fields,":[41],"which":[42,85],"dates":[43],"back":[44],"early":[47],"work":[48,120],"Laplace,":[50],"more":[53],"recent":[54],"seminal":[55],"contribution":[56],"Good":[58],"Turing.":[60],"Most":[61],"popular":[62],"schemes":[66,87],"are":[67,102],"universal,":[68],"sense":[71],"they":[73],"preform":[74],"well":[75],"for":[76,84],"every":[77],"possible":[78],"distribution.":[79],"Interestingly,":[80],"worst-case":[82,144],"distribution,":[83],"these":[86],"perform":[88],"worst,":[90],"known":[92],"be":[94,111],"uniform.":[95],"On":[96],"other":[98],"hand,":[99],"real-world":[100],"distributions":[101,137],"typically":[103],"heavy-tailed.":[104],"This":[105],"means":[106],"current":[108],"frameworks":[109],"may":[110],"over-pessimistic,":[112],"many":[114],"cases":[115],"interest.":[117],"In":[118],"this":[119,129],"we":[121,132],"suggest":[122],"data-dependent":[124],"scheme":[126,152],"address":[128],"caveat.":[130],"Specifically,":[131],"infer":[133],"subset":[135],"sample,":[140],"control":[142],"performance":[145,155],"only":[146],"subset.":[149],"Our":[150],"suggested":[151],"demonstrates":[153],"improved":[154],"guarantees":[156],"compared":[157],"alternative":[159],"methods.":[160]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
