Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates

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dc.creatorZhou, Yong
dc.creatorLiang, Hua
dc.date2009-03-03
dc.date.accessioned2026-07-07T12:48:39Z
dc.date.available2026-07-07T12:48:39Z
dc.descriptionWe study semiparametric varying-coefficient partially linear models when some linear covariates are not observed, but ancillary variables are available. Semiparametric profile least-square based estimation procedures are developed for parametric and nonparametric components after we calibrate the error-prone covariates. Asymptotic properties of the proposed estimators are established. We also propose the profile least-square based ratio test and Wald test to identify significant parametric and nonparametric components. To improve accuracy of the proposed tests for small or moderate sample sizes, a wild bootstrap version is also proposed to calculate the critical values. Intensive simulation experiments are conducted to illustrate the proposed approaches.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-AOS561 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0903.0499
dc.identifierhttp://arxiv.org/abs/0903.0499
dc.identifierAnnals of Statistics 2009, Vol. 37, No. 1, 427-458
dc.identifierdoi:10.1214/07-AOS561
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/222130
dc.subjectStatistics Theory
dc.subject62G08, 62G10 (Primary) 62G20, 62H15 (Secondary)
dc.titleStatistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates
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