Discovering interpretable structure in longitudinal predictors via coefficient trees

We consider the regression setting in which the response variable is not longitudinal (i.e., it is observed once for each case), but it is assumed to depend functionally on a set of predictors that are observed longitudinally, which is a specific form of functional predictors. In this situation, we...

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Bibliographic Details
Published in:	Advances in data analysis and classification Vol. 18; no. 4; pp. 911 - 951
Main Authors:	Sürer, Özge, Apley, Daniel W., Malthouse, Edward C.
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024 Springer Nature B.V
Subjects:	Basketball Chemistry and Earth Sciences Computer Science Data Mining and Knowledge Discovery Datasets Dimensional analysis Economics Finance Health Sciences Households Humanities Insurance Law Management Mathematics and Statistics Medicine Physics Regression analysis Regular Article Statistical Theory and Methods Statistics Statistics for Business Statistics for Engineering Statistics for Life Sciences Statistics for Social Sciences Time series Sequential data 62J05 Linear regression; mixed models Longitudinal predictors Functional data Interpretability 62-08 Computational methods for problems pertaining to statistics 62H99 None of the above, but in this section Group structure Pattern discovery
ISSN:	1862-5347, 1862-5355
Online Access:	Get full text
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