A1005
Title: A nonlinear mixed-effects functional regression model based on variable selection
Authors: Yan-fu Li - Tsinghua University (China) [presenting]
Abstract: The mixed-effects functional regression (MFR) model offers a valuable tool for analyzing dynamic data with individual-specific variations. However, challenges arise in scenarios with non-linear relationships and variable selection among covariates. A novel extension to the MFR model is proposed. The approach incorporates non-linear components using bivariate splines, enabling a robust framework for complex relationship modeling. For variable selection in high-dimensional regression, a group-minimax concave penalty (MCP) that treats parameters from the same spline basis as a group is employed, ensuring accurate and unbiased selection. This methodology allows for estimating fixed and random effects in a two-step procedure. A flexible and comprehensive framework accommodates non-linear covariates and an MCP variable selection approach. Empirical validations and theoretical justifications support the effectiveness of our proposed methodology. In summary, the approach provides a versatile and efficient tool for modeling functional responses in the presence of non-linear relationships and mixed effects.
