ABSTRACT

A rank-based estimation and selection of the functional regression coefficients for the single-index varying coefficient regression model is considered. The estimation of the functional regression coefficients together with the selection procedure are carried out following a proposed back-fitting type computational algorithm by minimizing the rank-based objective function. Finite sample performance of the proposed estimator are evaluated via extensive Monte Carlo simulation studies. These demonstrate the robustness and efficiency of the proposed estimator compared to its least squares counterpart under different settings of the model error distribution. A real data example motivated by problems in deep-water fish ecology is given to illustrate the proposed methodology.

摘要

考虑针对单指数变化系数回归模型的功能回归系数的基于等级的估计和选择。通过最小化基于等级的目标函数，根据提出的反向拟合类型计算算法，对功能回归系数的估计以及选择过程进行了估计。通过广泛的蒙特卡洛模拟研究评估了拟议估计量的有限样本性能。这些证明了在模型误差分布的不同设置下，与其最小二乘方相比，该估计器的鲁棒性和效率。给出了一个由深水鱼类生态学问题引起的真实数据示例，以说明所提出的方法。