A tool to determine the degrees of freedom in tree-structured varying coefficient models

TL;DR

This paper introduces a simple formula to accurately estimate the degrees of freedom in tree-structured varying coefficient models, improving model selection and predictive performance.

Contribution

It provides a novel, easy-to-apply DoF approximation formula for TSVC models, enhancing model selection accuracy over naive methods.

Findings

The proposed DoF formula improves model selection accuracy.

Using the formula leads to better predictive performance.

Simulation and real data applications demonstrate its effectiveness.

Abstract

The tree-structured varying coefficient (TSVC) model is a flexible approach for generalized regression, where the linear effects of the covariates are allowed to vary with the values of effect modifiers. Relevant effect modifiers and interactions are identified using recursive partitioning. In TSVC models, analogously to other semi- and nonparametric regression approaches, one needs to account for the cost of data-driven model building when deriving the model degrees of freedom (DoF). To address this issue, we develop an easy-to-apply formula to approximate the DoF of a TSVC model. This formula is employed for model selection based on the Bayesian information criterion (BIC) and compared to the naive solution, setting the DoF to the number of free model parameters, in a simulation study. To illustrate the proposed DoF method, TSVC models using BIC-based selection were fitted to data from the Survey of Health, Ageing, and Retirement in Europe. Results indicated that calculation of the DoF using the proposed formula resulted in more accurate selection results with improved predictive ability.