Estimation and testing for partially functional linear errors-in-variables models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 296-314 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 170 |
Online published | 17 Nov 2018 |
Publication status | Published - Mar 2019 |
Link(s)
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.
In: Journal of Multivariate Analysis, Vol. 170, 03.2019, p. 296-314.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review