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Recent advances in longitudinal data analysis
Fu, Liya Wang, You-Gan Wu, Jinran
Fu, Liya
Wang, You-Gan
Wu, Jinran
Author
Abstract
This work aims to provide a review of methodology on analysis of longitudinal data focusing on (i) how to select different model components: the covariance (correlation and variance) functions or structures, and the predictive variables; (ii) the robust approaches including rank and quantile regression; and (iii) machine learning algorithms that incorporate the temporal or clustering effects. Specifically, among longitudinal machine learning algorithms, tree-based methods are widely used for modeling random effects, while support vector machine-based techniques are adapted to include temporal structure and random effects. More recently, there has been an emerging interest in using (deep) neural networks trained with derived optimization objectives to capture complex patterns in longitudinal data.
Keywords
Correlation information criterion, Covariance modeling, Data-driven tuning parameter, Machine learning, Model selection, Penalty function, Quantile regression, Rank regression, Robust estimation, Working likelihood
Date
2024
Type
Journal article
Journal
Book
Volume
50
Issue
Page Range
173-221
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Institute for Positive Psychology and Education
Faculty of Education and Arts
Institute for Positive Psychology and Education
Relation URI
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled
Notes
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No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.