Functional regression with repeated eigenvalues

Matthew Reimherr

doi:10.1016/j.spl.2015.07.037

Functional regression with repeated eigenvalues

Matthew Reimherr

Statistics

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

We explore the functional principal component method for estimating regression parameters in functional linear models. We demonstrate that the commonly made assumption concerning unique eigenvalues is unnecessary. Convergence rates are established allowing a variety of sample spaces and dependence structures.

Original language	English (US)
Pages (from-to)	62-70
Number of pages	9
Journal	Statistics and Probability Letters
Volume	107
DOIs	https://doi.org/10.1016/j.spl.2015.07.037
State	Published - Dec 1 2015

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/j.spl.2015.07.037

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title = "Functional regression with repeated eigenvalues",

abstract = "We explore the functional principal component method for estimating regression parameters in functional linear models. We demonstrate that the commonly made assumption concerning unique eigenvalues is unnecessary. Convergence rates are established allowing a variety of sample spaces and dependence structures.",

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JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

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Functional regression with repeated eigenvalues

Abstract

All Science Journal Classification (ASJC) codes

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