Some Models and Their Extensions for Longitudinal Analyses

Tapio Nummi ()
Additional contact information
Tapio Nummi: Tampere University, Faculty of Information Technology and Communication Sciences

A chapter in Data Science and SDGs, 2021, pp 15-30 from Springer

Abstract: Abstract In this article, I present some of my statistical research in the field of longitudinal data analysis along with applications of these methods to real data sets. The aim is not to cover the whole field; rather, the perspective is based on my own personal preferences. The presented methods are mainly based on growth curve and mixture regression models and their extensions, where the focus is on continuous longitudinal data. In addition, an example of the analysis of extensive register data for categorical longitudinal data is presented. Applica-tions range from forestry and health sciences to social sciences.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-1919-9_2

Ordering information: This item can be ordered from
http://www.springer.com/9789811619199

DOI: 10.1007/978-981-16-1919-9_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().