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Self‐modelling warping functions
Author(s) -
Gervini Daniel,
Gasser Theo
Publication year - 2004
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2004.b5582.x
Subject(s) - overfitting , image warping , nonparametric statistics , computer science , component (thermodynamics) , inference , flexibility (engineering) , artificial intelligence , statistical inference , dynamic time warping , statistical model , pattern recognition (psychology) , machine learning , econometrics , mathematics , data mining , statistics , artificial neural network , thermodynamics , physics
Summary. The paper introduces a semiparametric model for functional data. The warping functions are assumed to be linear combinations of q common components, which are estimated from the data (hence the name ‘self‐modelling’). Even small values of q provide 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).