Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78186
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dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorLiu, Zen_US
dc.creatorLiu, Cen_US
dc.creatorSun, Zen_US
dc.date.accessioned2018-09-28T01:07:55Z-
dc.date.available2018-09-28T01:07:55Z-
dc.identifier.urihttp://hdl.handle.net/10397/78186-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2018 Elsevier B.V. All rights reserved.en_US
dc.rights© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Liu, Z., Liu, C., & Sun, Z. (2019). Consistent model check of errors-in-variables varying-coefficient model with auxiliary variable. Journal of Statistical Planning and Inference, 198, 13-28 is available at https://doi.org/10.1016/j.jspi.2018.03.002en_US
dc.subjectAuxiliary variableen_US
dc.subjectEmpirical processen_US
dc.subjectMeasurement erroren_US
dc.subjectModel checken_US
dc.subjectVarying-coefficient modelen_US
dc.titleConsistent model check of errors-in-variables varying-coefficient model with auxiliary variableen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage13en_US
dc.identifier.epage28en_US
dc.identifier.volume198en_US
dc.identifier.doi10.1016/j.jspi.2018.03.002en_US
dcterms.abstractIn this paper, we consider the adequacy check of the varying-coefficient model when covariates are measured with error and some auxiliary variable is available. With the help of auxiliary variable, we calibrate the measurement error and obtain an estimator of the unobservable true variable. The empirical-process-based test is built by applying the calibrated estimator of the model error. The asymptotic properties of the proposed test are rigorously investigated under the null hypothesis, local and global alternatives. It is shown that the proposed test is consistent and has good properties of power. We illustrate that the naive method cannot control Type I error and loses effect completely. But the proposed calibrated method performs well in terms of the empirical sizes close to the test level and high empirical powers. Simulation studies and two real data analyses are conducted to demonstrate the performance of the proposed approach.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of statistical planning and inference, Jan. 2019, v. 198, p. 13-28en_US
dcterms.isPartOfJournal of statistical planning and inferenceen_US
dcterms.issued2019-01-
dc.identifier.scopus2-s2.0-85044847892-
dc.identifier.eissn0378-3758en_US
dc.description.validate201809 bcmaen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberAMA-0324-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS27010224-
dc.description.oaCategoryGreen (AAM)en_US
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