{"id":"https://openalex.org/W2183063699","doi":null,"title":"Minimum Distance Estimation of Randomly Censored Regression Models with Endogeneity","display_name":"Minimum Distance Estimation of Randomly Censored Regression Models with Endogeneity","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W2183063699","mag":"2183063699"},"language":"en","primary_location":{"id":"mag:2183063699","doi":null,"is_oa":false,"landing_page_url":"https://www.econ.duke.edu/~staff/wrkshop_papers/2005-Fall/Khan.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5108337522","display_name":"Shakeeb A. Khan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shakeeb Khan","raw_affiliation_strings":["University of Rochester"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Rochester","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087851210","display_name":"Elie Tamer","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elie Tamer","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3228,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57593806,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10136","display_name":"Statistical Methods and Inference","score":0.861299991607666,"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":0.861299991607666,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.05730000138282776,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.048700001090765,"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/minimum-distance-estimation","display_name":"minimum distance estimation","score":0.9959999918937683},{"id":"https://openalex.org/keywords/endogeneity","display_name":"endogeneity","score":0.925000011920929},{"id":"https://openalex.org/keywords/endogenous-regressors","display_name":"endogenous regressors","score":0.8709999918937683},{"id":"https://openalex.org/keywords/duration-models","display_name":"duration models","score":0.7570000290870667},{"id":"https://openalex.org/keywords/competing-risks","display_name":"competing risks","score":0.7620000243186951},{"id":"https://openalex.org/keywords/conditional-heteroscedasticity","display_name":"conditional heteroskedasticity","score":0.8989999890327454},{"id":"https://openalex.org/keywords/covariate-dependent-censoring","display_name":"covariate-dependent censoring","score":0.8769999742507935},{"id":"https://openalex.org/keywords/asymptotic-normality","display_name":"asymptotic normality","score":0.8080000281333923},{"id":"https://openalex.org/keywords/parametric-rate-of-convergence","display_name":"parametric rate of convergence","score":0.8169999718666077},{"id":"https://openalex.org/keywords/drug-relapse","display_name":"drug relapse","score":0.828000009059906}],"concepts":[{"id":"https://openalex.org/C610760","wikidata":"https://www.wikidata.org/wiki/Q1340706","display_name":"Endogeneity","level":2,"score":0.8317252397537231},{"id":"https://openalex.org/C137668524","wikidata":"https://www.wikidata.org/wiki/Q189813","display_name":"Censoring (clinical trials)","level":2,"score":0.8210279941558838},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.7288621664047241},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7030392289161682},{"id":"https://openalex.org/C97379794","wikidata":"https://www.wikidata.org/wiki/Q4503892","display_name":"Censored regression model","level":3,"score":0.6893887519836426},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.6874181628227234},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6742606163024902},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6654878258705139},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6442047357559204},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.5409532189369202},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4668007493019104},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4535145163536072},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4147467613220215}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2183063699","doi":null,"is_oa":false,"landing_page_url":"https://www.econ.duke.edu/~staff/wrkshop_papers/2005-Fall/Khan.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1500520980","https://openalex.org/W1551610462","https://openalex.org/W1591629962","https://openalex.org/W1600156035","https://openalex.org/W1976652590","https://openalex.org/W1979300931","https://openalex.org/W1999075324","https://openalex.org/W2001433637","https://openalex.org/W2005747881","https://openalex.org/W2007850519","https://openalex.org/W2010029425","https://openalex.org/W2041638842","https://openalex.org/W2042253044","https://openalex.org/W2046165968","https://openalex.org/W2056099894","https://openalex.org/W2059053591","https://openalex.org/W2059154395","https://openalex.org/W2060198626","https://openalex.org/W2063953281","https://openalex.org/W2074291883","https://openalex.org/W2075900723","https://openalex.org/W2079510774","https://openalex.org/W2083194629","https://openalex.org/W2083972068","https://openalex.org/W2084360511","https://openalex.org/W2092835732","https://openalex.org/W2119918368","https://openalex.org/W2139255721","https://openalex.org/W2139772358","https://openalex.org/W2168334201","https://openalex.org/W2330138064","https://openalex.org/W2797333853","https://openalex.org/W3122272406","https://openalex.org/W3125441349"],"related_works":["https://openalex.org/W7096326361","https://openalex.org/W2045653036","https://openalex.org/W2018132457","https://openalex.org/W1993610348","https://openalex.org/W2041638842","https://openalex.org/W1634767542","https://openalex.org/W7097480282","https://openalex.org/W3139636836","https://openalex.org/W1974165644","https://openalex.org/W2356856490"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"minimum":[3],"distance":[4],"estimation":[5],"procedures":[6],"for":[7,58],"the":[8,38,42,45,49,76],"slope":[9],"coe\u2010cients":[10],"and":[11,24,48,66,89,94],"location":[12],"parameter":[13],"in":[14,22,34],"randomly":[15],"censored":[16],"regression":[17],"models":[18],"that":[19],"are":[20],"used":[21],"duration":[23],"competing":[25],"risk":[26],"models.":[27],"The":[28,69],"proposed":[29],"procedure":[30],"generalizes":[31],"existing":[32],"work":[33],"terms":[35],"of":[36,44,53],"weakening":[37],"restrictions":[39],"imposed":[40],"on":[41],"distribution":[43],"error":[46],"term":[47],"censoring":[50],"variable.":[51],"Examples":[52],"such":[54],"generalizations":[55],"include":[56],"allowing":[57],"conditional":[59],"heteroskedasticity,":[60],"covariate":[61],"dependent":[62],"censoring,":[63],"endogenous":[64,67],"regressors":[65],"censoring.":[68],"estimator":[70],"is":[71],"shown":[72],"to":[73],"converge":[74],"at":[75],"parametric":[77],"rate":[78],"with":[79],"an":[80],"asymptotic":[81],"normal":[82],"distribution.":[83],"A":[84],"small":[85],"scale":[86],"simulation":[87],"study":[88],"applications":[90],"using":[91],"drug":[92],"relapse":[93],"re-employment":[95],"bonus":[96],"data":[97],"demonstrate":[98],"satisfactory":[99],"flnite":[100],"sample":[101],"performance.":[102]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}