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Bibliographic Details
Main Author: Zhou, Zhiyang
Format: Preprint
Published: 2019
Subjects:
Online Access:https://arxiv.org/abs/1901.07599
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author Zhou, Zhiyang
author_facet Zhou, Zhiyang
contents Functional principal component regression (PCR) can fail to provide good prediction if the response is highly correlated with some excluded functional principal component(s). This situation is common since the construction of functional principal components never involves the response. Aiming at this shortcoming, we develop functional continuum regression (CR). The framework of functional CR includes, as special cases, both functional PCR and functional partial least squares (PLS). Functional CR is expected to own a better accuracy than functional PCR and functional PLS both in estimation and prediction; evidence for this is provided through simulation and numerical case studies. Also, we demonstrate the consistency of estimators given by functional CR.
format Preprint
id arxiv_https___arxiv_org_abs_1901_07599
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle Functional continuum regression
Zhou, Zhiyang
Methodology
62G08
Functional principal component regression (PCR) can fail to provide good prediction if the response is highly correlated with some excluded functional principal component(s). This situation is common since the construction of functional principal components never involves the response. Aiming at this shortcoming, we develop functional continuum regression (CR). The framework of functional CR includes, as special cases, both functional PCR and functional partial least squares (PLS). Functional CR is expected to own a better accuracy than functional PCR and functional PLS both in estimation and prediction; evidence for this is provided through simulation and numerical case studies. Also, we demonstrate the consistency of estimators given by functional CR.
title Functional continuum regression
topic Methodology
62G08
url https://arxiv.org/abs/1901.07599