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Regularized Nonlinear Regression with Dependent Errors and its Application to a Biomechanical Model
- Source :
- Annals of the Institute of Statistical Mathematics, 2024
- Publication Year :
- 2022
-
Abstract
- A biomechanical model often requires parameter estimation and selection in a known but complicated nonlinear function. Motivated by observing that data from a head-neck position tracking system, one of biomechanical models, show multiplicative time dependent errors, we develop a modified penalized weighted least squares estimator. The proposed method can be also applied to a model with non-zero mean time dependent additive errors. Asymptotic properties of the proposed estimator are investigated under mild conditions on a weight matrix and the error process. A simulation study demonstrates that the proposed estimation works 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).<br />Comment: The article revised in overall
- Subjects :
- Statistics - Methodology
Mathematics - Statistics Theory
Subjects
Details
- Database :
- arXiv
- Journal :
- Annals of the Institute of Statistical Mathematics, 2024
- Publication Type :
- Report
- Accession number :
- edsarx.2210.13550
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1007/s10463-023-00895-1