标题： 非凹惩罚样条 显示英文标题

标题： Nonconcave Penalized Spline

摘要： 回归样条是非参数回归的一个有用工具。然而，寻找最优节点位置是一个已知的难题。本文介绍了非凹惩罚回归样条方法。该方法不仅能生成具有最优收敛速度的平滑样条，还能同时自适应地选择最优节点。它对原始节点的数量不敏感。研究了该方法在模拟中的表现，以与其他方法进行比较。讨论了如何选择平滑参数（即非凹回归样条中的惩罚参数）的问题。 显示英文摘要

摘要： Regression spline is a useful tool in nonparametric regression. However, finding the optimal knot locations is a known difficult problem. In this article, we introduce the Non-concave Penalized Regression Spline. This proposal method not only produces smoothing spline with optimal convergence rate, but also can adaptively select optimal knots simultaneously. It is insensitive to the number of origin knots. The method's performance in a simulation has been studied to compare the other methods. The problem of how to choose smoothing parameters, i.e. penalty parameters in the non-concave regression spline is addressed.