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

Piotr Kokoszka; Matthew Reimherr

doi:10.1177/1471082X16681331

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

Piotr Kokoszka, Matthew Reimherr

Statistics

Research output: Contribution to journal › Comment/debate › peer-review

1 Scopus citations

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 language	English (US)
Pages (from-to)	45-49
Number of pages	5
Journal	Statistical Modelling
Volume	17
Issue number	1-2
DOIs	https://doi.org/10.1177/1471082X16681331
State	Published - Feb 1 2017

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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title = "Discussion of {\textquoteleft}a general framework for functional regression modelling{\textquoteright} 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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AB - 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

Abstract

All Science Journal Classification (ASJC) codes

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