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Self-modelling warping functions


Gervini, D; Gasser, T (2004). Self-modelling warping functions. Journal of the Royal Statistical Society. Series B, 66(4):959-971.

Abstract

The paper introduces a semiparametric model for functional data. The warping functions are assumed to be linear combinations ofqcommon components, which are estimated from the data (hence the name‘self-modelling’). Even small values ofqprovide remarkable model flexibility, comparable with nonparametric methods. At the same time, this approach avoids overfitting because the common components are estimated combining data across individuals. As a convenient by-product, component scores are often interpretable and can be used for statistical inference (an example of classification based on scores is given). [ABSTRACT FROM AUTHOR]

Abstract

The paper introduces a semiparametric model for functional data. The warping functions are assumed to be linear combinations ofqcommon components, which are estimated from the data (hence the name‘self-modelling’). Even small values ofqprovide remarkable model flexibility, comparable with nonparametric methods. At the same time, this approach avoids overfitting because the common components are estimated combining data across individuals. As a convenient by-product, component scores are often interpretable and can be used for statistical inference (an example of classification based on scores is given). [ABSTRACT FROM AUTHOR]

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Date:2004
Deposited On:22 Jun 2009 15:14
Last Modified:05 Apr 2016 13:16
Publisher:Wiley-Blackwell
ISSN:1369-7412
Publisher DOI:https://doi.org/10.1111/j.1467-9868.2004.B5582.x

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