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Updated: Feb 22, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Mixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data
1Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu
Abstract:
In this article, I review the use of nonparametric methods in the analysis of longitudinal and growth curve data, particularly the multivariate adaptive splines models for the analysis of longitudinal data (MASAL). These methods combine nonparametric techniques (B-splines, kernel smoothing, piecewise polynomials) and models with random effects, and provide fruitful alternatives to mixed effects linear models. Similarities, differences, strengths and limitations among these methods are presented. The analysis of a real example is also presented to illustrate the application and interpretation of MASAL. Open questions are posed for further investigation.
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