{"id":"https://openalex.org/W2972362476","doi":"https://doi.org/10.1145/3343485.3343489","title":"Estimation and Use of Correlation in Multiple Hypothesis Testing with High Dimensional Data","display_name":"Estimation and Use of Correlation in Multiple Hypothesis Testing with High Dimensional Data","publication_year":2019,"publication_date":"2019-07-08","ids":{"openalex":"https://openalex.org/W2972362476","doi":"https://doi.org/10.1145/3343485.3343489","mag":"2972362476"},"language":"en","primary_location":{"id":"doi:10.1145/3343485.3343489","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3343485.3343489","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 2nd International Conference on Mathematics and Statistics","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/A5112449992","display_name":"Pianpool Kirdwichai","orcid":null},"institutions":[{"id":"https://openalex.org/I82828225","display_name":"King Mongkut's University of Technology North Bangkok","ror":"https://ror.org/04fy6jb97","country_code":"TH","type":"education","lineage":["https://openalex.org/I82828225"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Pianpool Kirdwichai","raw_affiliation_strings":["King Mongkut's University of Technology North Bangkok, Bangkok, Thailand"],"affiliations":[{"raw_affiliation_string":"King Mongkut's University of Technology North Bangkok, Bangkok, Thailand","institution_ids":["https://openalex.org/I82828225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5112449992"],"corresponding_institution_ids":["https://openalex.org/I82828225"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0827516,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"36","last_page":"39"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10261","display_name":"Genetic Associations and Epidemiology","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6391907930374146},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.5765352249145508},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5446822047233582},{"id":"https://openalex.org/keywords/multiple-comparisons-problem","display_name":"Multiple comparisons problem","score":0.5005550384521484},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.48823240399360657},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4665643274784088},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.46240881085395813},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.4325605034828186},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43000781536102295},{"id":"https://openalex.org/keywords/bonferroni-correction","display_name":"Bonferroni correction","score":0.429923415184021},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4290291666984558},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.40762490034103394},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3445795476436615},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25255468487739563},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19779422879219055},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.13314741849899292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6391907930374146},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.5765352249145508},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5446822047233582},{"id":"https://openalex.org/C183905921","wikidata":"https://www.wikidata.org/wiki/Q1038757","display_name":"Multiple comparisons problem","level":2,"score":0.5005550384521484},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48823240399360657},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4665643274784088},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.46240881085395813},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.4325605034828186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43000781536102295},{"id":"https://openalex.org/C127808970","wikidata":"https://www.wikidata.org/wiki/Q385989","display_name":"Bonferroni correction","level":2,"score":0.429923415184021},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4290291666984558},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.40762490034103394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3445795476436615},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25255468487739563},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19779422879219055},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.13314741849899292},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3343485.3343489","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3343485.3343489","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 2nd International Conference on Mathematics and Statistics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2101759642","https://openalex.org/W2105239414","https://openalex.org/W2107755054","https://openalex.org/W2121044470","https://openalex.org/W2126271654","https://openalex.org/W2140253369","https://openalex.org/W3103280585"],"related_works":["https://openalex.org/W2093104131","https://openalex.org/W4293336298","https://openalex.org/W2333750400","https://openalex.org/W2034495139","https://openalex.org/W2104806243","https://openalex.org/W2467227750","https://openalex.org/W4250330777","https://openalex.org/W4235821062","https://openalex.org/W1970047903","https://openalex.org/W2903672469"],"abstract_inverted_index":{"Within":[0],"recent":[1],"years,":[2],"cheaper,":[3],"continually":[4],"improving":[5],"sequencing":[6],"and":[7,37,44,140,145,153,175,200,212,216,236],"genotyping":[8],"techniques":[9],"have":[10],"resulted":[11],"in":[12,15,29,126,146,157,206,209],"an":[13],"explosion":[14],"high":[16],"dimensional":[17],"genomic":[18,210],"data.":[19],"While":[20],"these":[21],"information-rich":[22],"datasets":[23],"are":[24],"welcome":[25],"as":[26,32,167,186],"potentially":[27],"useful":[28],"areas":[30],"such":[31,166],"studies":[33],"of":[34,83,90,97,107,134,138,148,151,159,163,181,241],"complex":[35,197],"diseases":[36],"pharmacogenetics,":[38],"computational":[39],"difficulties":[40],"is":[41,56,75,101],"storage,":[42],"retrieval":[43],"analysis":[45,164],"mean":[46],"that":[47,110,230],"extracting":[48],"the":[49,68,72,80,88,94,135,160,179,239],"information":[50,208],"relevant":[51],"to":[52,58,77,87,190,198,219],"specific":[53],"research":[54],"questions":[55],"proving":[57],"be":[59,191],"more":[60],"difficult":[61],"than":[62],"perhaps":[63],"was":[64,123,131],"originally":[65],"anticipated.":[66],"From":[67],"statistical":[69],"modelling":[70],"perspective,":[71],"primary":[73],"challenge":[74],"how":[76],"deal":[78],"with":[79,105],"small":[81],"number":[82,89,240],"individuals":[84],"studied,":[85],"relative":[86],"observed":[91],"explanatory":[92],"factors;":[93],"so-called":[95],"\"curse":[96],"dimensionality\"'.":[98],"An":[99],"example":[100],"cancer":[102],"pharmacogenomics,":[103],"concerned":[104],"identification":[106],"genetic":[108,128],"variants":[109],"influence":[111],"drug":[112],"responses.":[113],"Inference":[114],"based":[115,226],"on":[116,227],"multiple":[117],"hypothesis":[118],"testing":[119],"(multiple":[120],"comparison)":[121],"procedures":[122],"initially":[124],"preferred":[125],"high-dimensional":[127],"studies.":[129],"This":[130,202],"mainly":[132],"because":[133],"comparative":[136],"simplicity":[137],"Bonferroni":[139],"related":[141],"error":[142],"correction":[143],"methods,":[144,178],"spite":[147,158],"well-known":[149],"limitation":[150],"conservativeness":[152],"inefficient":[154,182],"analyses.":[155],"Currently,":[156],"substantial":[161],"availability":[162],"methodology,":[165],"p-value":[168],"aggregation,":[169],"dimension":[170],"reduction,":[171],"variable":[172],"selection,":[173],"pooling":[174],"Bayesian":[176],"MCMC":[177],"problem":[180],"inference":[183],"remains":[184],"unresolved":[185],"current":[187],"methods":[188],"tend":[189],"either":[192],"highly":[193],"computationally":[194],"demanding":[195],"or":[196],"implement":[199],"interpret.":[201],"talk":[203],"highlights":[204],"issues":[205],"interpreting":[207],"data":[211],"presents":[213],"both":[214],"theoretical":[215],"simulation":[217],"results":[218],"argue":[220],"for":[221],"a":[222],"novel":[223],"inferential":[224],"solution":[225],"nonparametric":[228],"regression":[229],"can":[231],"reliably":[232],"identify":[233],"true":[234],"positives":[235],"simultaneously":[237],"minimize":[238],"spurious":[242],"findings.":[243]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}