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响应变量右删失时参数分位数回归模型的模型检验.docx

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响应变量右删失时参数分位数回归模型的模型检验 Title:ModelTestingforQuantileRegressionwhentheResponseVariableisRight-Censored Abstract: Quantileregressionisapowerfultoolforanalyzingtherelationshipbetweencovariatesandconditionalquantilesoftheresponsevariable.However,insomecases,theresponsevariablemaybesubjecttorightcensoring,wherethevaluesexceedingacertainthresholdaretruncatedorunknown.Thispaperfocusesonmodeltestinginthecontextofquantileregressionwhentheresponsevariableisright-censored.Wediscussthechallengesposedbycensoringandproposeaframeworkforconductingmodeltestsinsuchscenarios.Specifically,weexplorevarioustestsbasedondiagnosticmeasures,simulateddata,andresamplingtechniques.TheperformanceofthesetestsisevaluatedthroughMonteCarlosimulations,andacasestudyispresentedtoillustratetheirapplicationonrealdata. 1.Introduction Quantileregressionextendstraditionallinearregressionbyestimatingtheconditionalquantilesoftheresponsevariableratherthantheconditionalmean.Thisapproachprovidesamorecomprehensiveunderstandingoftherelationshipbetweenthecovariatesanddifferentquantilesoftheresponsevariable,allowingformorenuancedanalysis.However,inscenarioswheretheresponsevariableissubjecttorightcensoring,theusualtechniquesforquantileregressionarenolongerdirectlyapplicable.Rightcensoringoccurswhentheresponsevaluesareknowntobeaboveacertainthresholdbutareotherwiseunobserved.Insuchcases,theestimationandinterpretationofquantileregressionmodelsrequirespecialconsideration. 2.ChallengesinQuantileRegressionwithRightCensoring Toaddressthechallengesposedbyrightcensoringinquantileregression,variousapproacheshavebeenproposedintheliterature.Onecommonapproachinvolvesimputingthecensoredobservationsusingsurvivalanalysistechniques,allowingformoreaccurateestimationofthequantiles.However,thisintroducesadditionaluncertaintyintheanalysisandrequirespropervalidationoftheimputationmethods.Anotherapproachistouseinverseprobabilityweightingtoadjustthequantileestimatesbasedontheproportionofcensoredobservations.Thismethodaccountsforcensoringexp