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Erschienen in:
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2022 | OriginalPaper | Buchkapitel

Functional Linear Regression for Partially Observed Functional Data

verfasst von : Yafei Wang, Tingyu Lai, Bei Jiang, Linglong Kong, Zhongzhan Zhang

Erschienen in: Advances and Innovations in Statistics and Data Science

Verlag: Springer International Publishing

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Abstract

In functional linear regression model, many methods have been proposed and studied to estimate the slope function while the functional predictor was observed in the entire domain. However, works on functional linear regression model with partially observed trajectories have received less attention. In this paper, to fill the literature gap we consider the scenario where individual functional predictor maybe observed only on part of the domain. Depending on whether measurement error is presented in functional predictors, two methods are developed, one is based on linear functionals of the observed part of the trajectory and the other one uses conditional principal component scores. We establish the asymptotic properties of the two proposed methods. Finite sample simulations are conducted to verify their performance. Diffusion tensor imaging (DTI) data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) study is analyzed.

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Vorheriges Kapitel A Robust Approach to Statistical Quality Control for High-Dimensional Non-Normal Data
Nächstes Kapitel Profile Estimation of Generalized Semiparametric Varying-Coefficient Additive Models for Longitudinal Data with Within-Subject Correlations
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Metadaten
Titel
Functional Linear Regression for Partially Observed Functional Data
verfasst von
Yafei Wang
Tingyu Lai
Bei Jiang
Linglong Kong
Zhongzhan Zhang
Copyright-Jahr
2022
Buch
Advances and Innovations in Statistics and Data Science

Print ISBN: 978-3-031-08328-0

Electronic ISBN: 978-3-031-08329-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-031-08329-7_7

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