2022/01/25 by Xufei Wang, Bo Jiang, Wang, Xufei +3
Engineering · Materials Science · Medicine · #Advanced Numerical Analysis Techniques #FOS: Computer and information sciences #Machine Learning in Materials Science #Methodology (stat.ME) #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.48550/arxiv.2201.10063
openalex publication_date 2022/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The varying coefficient model has received broad attention from researchers as it is a powerful dimension reduction tool for non-parametric modeling. Most existing varying coefficient models fitted with polynomial spline assume equidistant knots and take the number of knots as the hyperparameter. However, imposing equidistant knots appears to be too rigid, and determining the optimal number of knots systematically is also a challenge. In this article, we deal with this challenge by utilizing polynomial splines with adaptively selected and predictor-specific knots to fit the coefficients in varying coefficient models. An efficient dynamic programming algorithm is proposed to find the optimal solution. Numerical results show that the new method can achieve significantly smaller mean squared errors for coefficients compared with the equidistant spline fitting method.