# Linear classification methods for multivariate repeated measures data -- a simulation study

arXiv: https://arxiv.org/abs/2310.00107 · HTML: https://mlsanity.com/paper/2310.00107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2023.

## Built on by (most cited first, 1 of 1)

- [Comparison of the Cox proportional hazards model and Random Survival Forest algorithm for predicting patient-specific survival probabilities in clinical trial data](https://mlsanity.com/paper/2502.03119/) (2025)

## Abstract (arXiv)

Researchers in the behavioral and social sciences use linear discriminant analysis (LDA) for predictions of group membership (classification) and for identifying the variables most relevant to group separation among a set of continuous correlated variables (description).  In these and other disciplines, longitudinal data are often collected which provide additional temporal information. Linear classification methods for repeated measures data are more sensitive to actual group differences by taking the complex correlations between time points and variables into account, but are rarely discussed in the literature. Moreover, psychometric data rarely fulfill the multivariate normality assumption. In this paper, we compare existing linear classification algorithms for nonnormally distributed multivariate repeated measures data in a simulation study based on psychological questionnaire data comprising Likert scales. The results show that in data without any specific assumed structure and larger sample sizes, the robust alternatives to standard repeated measures LDA may not be needed. To our knowledge, this is one of the few studies discussing repeated measures classification techniques, and the first one comparing multiple alternatives among each other.