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关于条件生长曲线方法的讨论
- 研究问题
- 所给讨论关注条件生长曲线如何理解生长落后，以及未惩罚样条、条件与边际纵向建模和追赶建模有哪些问题。
- 方法与思路
- 讨论者评述Wei与He的分位数回归生长曲线方法，比较不同样条思路，并提出条件建模与替代追赶模型的讨论方向。
- 材料陈述的贡献
- 片段的贡献是提出方法审视与建模建议，尤其质疑未惩罚样条的实践灵活性与渐近理论联系；未呈现完整新定理。
- 阅读关注点
- 应结合被讨论论文核对条件分位数的定义、样条惩罚建议的依据，以及纵向条件模型和追赶指标的具体差别。
- 阅读边界
- 所给内容是截断的讨论文章片段，索引版本未注明；不能冒充主论文摘要，也不能把讨论者的意见当作已证明结果。
原文摘要
Overview.Wei and He are to be congratulated on an innovative and important article.The conditional approach to growth charts described in their article is important in a practical sense, and the use of quantile regression is both natural and well motivated.We look forward to further application of their idea to actual practice, because the concept of "falling behind" in one's growth cycle has two meanings: the usual standard growth chart, and the conditional growth chart described here.We have described the Wei and He approach to pediatricians, and they all grasped the essential clever idea immediately and were enthusiastic about the idea.Our commentary will focus on three aspects of the approach used by the authors, specifically (a) the use of unpenalized B-splines as described by the authors; (b) conditional versus marginal semiparametric modeling of longitudinal data; and (c) some alternative modeling approaches to "catch-up" that may get at the issue more directly and flexibly. B-splines should be penalized.One purpose of discussions, of course, is to make things a little lively, and here is our contribution.In our view, one should have some skepticism of how nonparametric unpenalized B-splines and unpenalized regression splines really are in the context of nonparametric regression.More precisely, and less inflammatory, the connection between asymptotic theory for unpenalized splines and actually attempting to be at least reasonably nonparametric is not at all clear.There is obviously a need to balance practical behavior and ease of implementation with theory.Kernel methods (see below) are one means of doing this.In the spline context, there are four approaches: smoothing splines, penalized regression splines, unpenalized regression splines and free-knot splines; see [18] for a recent review.As authors, we have observed the following.• Smoothing splines are basically penalized regression splines that place a knot at every value of the covariate.We have no idea how to do asymptotic theory for