A0186
Title: Regularized nonlinear regression with dependent errors and its application to a biomechanical model
Authors: Wei-Ying Wu - National Dong Hwa University (Taiwan) [presenting]
Abstract: A biomechanical model often requires parameter estimation and selection in a known but complicated nonlinear function. Motivated by observing that the data from a head-neck position tracking system, one of the biomechanical models, show multiplicative time-dependent errors, we develop a modified penalized weighted least squares estimator that can handle such error structure. The proposed method can also be applied to a model with non-zero meantime-dependent additive errors. Asymptotic properties of the proposed estimator are investigated. A simulation study demonstrates that the proposed estimation performs well in both parameter estimation and selection with time-dependent error. The analysis and comparison with an existing method for head-neck position tracking data show better performance of the proposed method in terms of the variance accounted for (VAF).
