LASSO regression
Research Methods
Definition. LASSO (least absolute shrinkage and selection operator) is a penalised regression method that shrinks coefficients and sets some to zero, performing variable selection.
Explanation
Tibshirani introduced the method (Tibshirani, 1996). In health services research, it is one of several strategies for selecting variables in machine learning models (Dong et al., 2025), and it is combined with path analysis to study student resilience (Su et al., 2026).
Related concepts
Machine learning Random forest Sample size and statistical power
Read on The Health Topics
- How to Choose the Right Variables in Health Services Machine Learning
- Predicting and Explaining Medical Students’ Psychological Resilience: A Dual Perspective of Machine Learning and Path Analysis
References
Dong, W., Lal, T., Liu, F., Pronovost, P., Bora, S., & Hoehn, R. S. (2025). Methodological considerations for optimal variable selection in machine learning for health services research. Health Services and Outcomes Research Methodology. https://doi.org/10.1007/s10742-025-00347-8
Su, W., Jia, H., Chang, W., Jiang, S., Dong, S., Ge, H., Qi, Y., Li, X., & Ma, G. (2026). The secrets of medical students’ psychological resilience: A dual perspective of machine learning and path analysis. International Journal of Medical Informatics, 205, 106111. https://doi.org/10.1016/j.ijmedinf.2025.106111
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
Cite this entry: Kurutkan, M. N. (2026). LASSO regression. In The Health Topics glossary. https://healthtopic.org/glossary/lasso-regression/