Estimation and testing for partially functional linear errors-in-variables models

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

28 Scopus Citations
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Author(s)

  • Hanbing Zhu
  • Riquan Zhang
  • Zhou Yu
  • Heng Lian
  • Yanghui Liu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)296-314
Journal / PublicationJournal of Multivariate Analysis
Volume170
Online published17 Nov 2018
Publication statusPublished - Mar 2019

Abstract

This paper considers estimation and testing problems for partial functional linear models when the covariates in the non-functional linear component are measured with additive error. A corrected profile, least-squares based, estimation procedure is developed for the parametric component. Asymptotic properties of the proposed estimators are established under some regularity conditions. To test a hypothesis on the parametric component, a statistic based on the difference between the corrected residual sums of squares under the null and alternative hypotheses is proposed; its limiting null distribution is shown to be a weighted sum of independent standard χ12 variables. Simulation studies are conducted to demonstrate the performance of the proposed procedure and a real example is analyzed for illustration.

Research Area(s)

  • Corrected profile least-squares, Errors-in-variables, Functional data, Hypothesis test, Partially linear models

Citation Format(s)

Estimation and testing for partially functional linear errors-in-variables models. / Zhu, Hanbing; Zhang, Riquan; Yu, Zhou et al.
In: Journal of Multivariate Analysis, Vol. 170, 03.2019, p. 296-314.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review