论文标题
依赖错误及其应用于生物力学模型的正规化非线性回归
Regularized Nonlinear Regression with Dependent Errors and its Application to a Biomechanical Model
论文作者
论文摘要
生物力学模型通常需要在已知但复杂的非线性函数中进行参数估计和选择。通过观察头颈位置跟踪系统的数据，生物力学模型之一显示了乘法性依赖性误差，我们开发了一个修改的惩罚加权最小二乘估计器。所提出的方法也可以应用于具有非零平均时间依赖性添加误差的模型。在轻度条件下，在重量矩阵和误差过程中研究了所提出的估计量的渐近特性。一项仿真研究表明，所提出的估计在参数估计和选择方面与时间相关误差都很好。与现有的头颈位置跟踪数据的分析和比较显示了所提出的方法的更好性能（VAF）。
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).
