{"id":"https://openalex.org/W2811333041","doi":null,"title":"A New Approach of Point Estimation and its Application to Truncated Data Situations","display_name":"A New Approach of Point Estimation and its Application to Truncated Data Situations","publication_year":2008,"publication_date":"2008-02-15","ids":{"openalex":"https://openalex.org/W2811333041","mag":"2811333041"},"language":"en","primary_location":{"id":"mag:2811333041","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/0802.2155v1","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/abs/0802.2155v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072119870","display_name":"Ahmed Guellil","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed Guellil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5089753035","display_name":"Tewfik Kernane","orcid":"https://orcid.org/0000-0002-8423-567X","observed_orcids":["https://orcid.org/0000-0002-8423-567X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tewfik Kernane","raw_affiliation_strings":[],"raw_orcid":null,"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":0,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.8779000043869019,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.8779000043869019,"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":0.11739999800920486,"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/T13141","display_name":"Statistical Methods and Applications","score":0.0007999999797903001,"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/point-estimation","display_name":"point estimation","score":0.9380000233650208},{"id":"https://openalex.org/keywords/truncated-data","display_name":"truncated data","score":0.7829999923706055},{"id":"https://openalex.org/keywords/minimum-distance-estimation","display_name":"minimum distance estimation","score":0.8870000243186951},{"id":"https://openalex.org/keywords/distribution-distance","display_name":"distance between distributions","score":0.7129999995231628},{"id":"https://openalex.org/keywords/parameter-estimation","display_name":"parameter estimation","score":0.5659999847412109},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.7739999890327454},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.9089999794960022},{"id":"https://openalex.org/keywords/goodness-of-fit-tests","display_name":"goodness-of-fit tests","score":0.7310000061988831},{"id":"https://openalex.org/keywords/grouped-data","display_name":"grouped data","score":0.3440000116825104},{"id":"https://openalex.org/keywords/continuous-probability-distributions","display_name":"continuous distributions","score":0.3930000066757202}],"concepts":[{"id":"https://openalex.org/C106195933","wikidata":"https://www.wikidata.org/wiki/Q7847935","display_name":"Truncation (statistics)","level":2,"score":0.6671883463859558},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6124987006187439},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4838598966598511},{"id":"https://openalex.org/C98385598","wikidata":"https://www.wikidata.org/wiki/Q1339385","display_name":"Empirical distribution function","level":2,"score":0.48369723558425903},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.48086386919021606},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4587978720664978},{"id":"https://openalex.org/C132480984","wikidata":"https://www.wikidata.org/wiki/Q2034239","display_name":"Goodness of fit","level":2,"score":0.4366389513015747},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.43641844391822815},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39143162965774536},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38278812170028687},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3681004047393799},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.354461669921875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2182694375514984},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:2811333041","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/0802.2155v1","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},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.242.8815","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.242.8815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/0802.2155v1.pdf","raw_type":"text"}],"best_oa_location":{"id":"mag:2811333041","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/0802.2155v1","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1496057935","https://openalex.org/W1583015473","https://openalex.org/W1861937314","https://openalex.org/W1979300931","https://openalex.org/W1986010085","https://openalex.org/W2024244628","https://openalex.org/W2049633694","https://openalex.org/W2118020555","https://openalex.org/W2122759946","https://openalex.org/W2155944434"],"related_works":["https://openalex.org/W1647834526","https://openalex.org/W4377471354","https://openalex.org/W7018975627","https://openalex.org/W1619108886","https://openalex.org/W2737183755","https://openalex.org/W6958252009","https://openalex.org/W6903800028","https://openalex.org/W7100537944","https://openalex.org/W1985542653","https://openalex.org/W2794431142"],"abstract_inverted_index":{"We":[0,107,170,188],"propose":[1,124],"a":[2,10,30,42,145,158,223],"new":[3,18,31,232],"approach":[4,173,196],"for":[5,55],"estimating":[6],"the":[7,27,34,59,65,82,86,91,95,99,103,110,113,117,127,133,141,148,151,161,164,168,172,180,183,186,195,205,210,215,231],"parameters":[8,93,111],"of":[9,20,29,38,45,61,85,94,98,135,147,150,163,185,194,204,217,225,227,235,241],"probability":[11],"distribution.":[12],"It":[13],"consists":[14],"on":[15,26,41,51,90,160,182,230],"combining":[16],"two":[17,39,128],"methods":[19],"estimation.":[21],"The":[22],"first":[23,87,142],"is":[24],"based":[25,229],"definition":[28],"distance":[32],"measuring":[33],"difference":[35],"between":[36],"variations":[37],"distributions":[40],"finite":[43],"number":[44,240],"points":[46],"from":[47,101,112,200,246,259],"their":[48],"support":[49,84],"and":[50,88,130,139,219,237,251],"using":[52],"this":[53],"measure":[54],"estimation":[56,203],"purposes":[57],"by":[58,116],"method":[60],"minimum":[62],"distance.":[63],"For":[64],"second":[66],"method,":[67],"given":[68],"an":[69,76],"empirical":[70,104,114],"discrete":[71,78],"distribution,":[72],"we":[73,123,131,155],"build":[74],"up":[75],"auxiliary":[77],"theoretical":[79,137],"distribution":[80,97,105,115],"having":[81],"same":[83,92],"depending":[89],"parent":[96],"data":[100,176,245],"which":[102],"emanated.":[106],"estimate":[108],"then":[109],"usual":[118],"statistical":[119],"methods.":[120],"In":[121],"practice,":[122],"to":[125,174,178],"compute":[126],"estimations":[129,236],"choose":[132],"second,":[134],"known":[136],"properties,":[138],"let":[140],"being":[143],"as":[144],"control":[146],"effectiveness":[149,184],"obtained":[152],"estimation,":[153],"so":[154],"have":[156],"also":[157,190],"criterion":[159],"quality":[162,234],"information":[165],"contained":[166],"in":[167,209,214,243],"observations.":[169],"apply":[171],"truncated":[175,201,260],"situations":[177],"give":[179,189],"flavour":[181],"approach.":[187],"some":[191],"interesting":[192],"perspectives":[193],"including":[197],"model":[198],"selection":[199,258],"data,":[202,238],"initial":[206],"trial":[207],"value":[208],"celebrate":[211],"EM":[212,253],"algorithm":[213],"case":[216],"truncation":[218],"merged":[220],"normal":[221],"populations,":[222],"test":[224],"goodness":[226],"fit":[228],"distance,":[233,256],"optimum":[239],"classes":[242],"grouping":[244],"continuous":[247],"distributions.":[248],"Key":[249],"words":[250],"phrases:":[252],"algorithm,":[254],"Minimum":[255],"Model":[257]},"counts_by_year":[],"updated_date":"2026-09-26T08:19:03.415552","created_date":"2018-07-10T00:00:00"}
