Homogeneity and Smoothness Analysis for Quantifying a Longitudinal Categorical Variable

Kohei Adachi
Additional contact information
Kohei Adachi: Koshien University

A chapter in Measurement and Multivariate Analysis, 2002, pp 47-56 from Springer

Abstract: Summary A variant of homogeneity analysis is proposed to analyze longitudinal data that describe the categories chosen by individuals at each of time-points. In the proposed method, individuals are given the scores that are natural cubic spline functions of time, and categories are given time-invariant scores. Loss of homogeneity between individual and category scores is combined with loss of smoothness of individual scores, to form a penalized loss function. It is minimized using eigenvalue or singular value decomposition. The weight of smoothness relative to homogeneity is chosen by a cross-validation procedure. The resulting scores yield a graphical representation of individuals’ trajectories with category points, which allows us to grasp longitudinal changes in individuals.

Keywords: Penalty Function; Spline Function; Correct Classification Rate; Optimal Scaling; Category Point (search for similar items in EconPapers)
Date: 2002
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:978-4-431-65955-6_5

Ordering information: This item can be ordered from
http://www.springer.com/9784431659556

DOI: 10.1007/978-4-431-65955-6_5

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 ().