Discussion of ‘a general framework for functional regression modelling’ by Greven and Scheipl

Piotr Kokoszka, Matthew Logan Reimherr

Research output: Contribution to journalComment/debate

Abstract

We discuss the challenge in properly assessing the uncertainty of the estimates produced by the R package pffr, especially as it pertains to constructing confidence bands and computing p-values in functional linear models. We also present an approach that partially addresses some of these issues. Simulations are provided to help articulate these ideas.

Original languageEnglish (US)
Pages (from-to)45-49
Number of pages5
JournalStatistical Modelling
Volume17
Issue number1-2
DOIs
StatePublished - Feb 1 2017

Fingerprint

Functional Linear Model
Confidence Bands
p-Value
Regression
Uncertainty
Computing
Modeling
Estimate
Simulation
Framework
Confidence
P value

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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title = "Discussion of ‘a general framework for functional regression modelling’ by Greven and Scheipl",
abstract = "We discuss the challenge in properly assessing the uncertainty of the estimates produced by the R package pffr, especially as it pertains to constructing confidence bands and computing p-values in functional linear models. We also present an approach that partially addresses some of these issues. Simulations are provided to help articulate these ideas.",
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Discussion of ‘a general framework for functional regression modelling’ by Greven and Scheipl. / Kokoszka, Piotr; Reimherr, Matthew Logan.

In: Statistical Modelling, Vol. 17, No. 1-2, 01.02.2017, p. 45-49.

Research output: Contribution to journalComment/debate

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