Fast estimation of regression parameters in a broken-stick model for longitudinal data.
Fast estimation of regression parameters in a broken-stick model for longitudinal data.
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DOI：
10.1080/01621459.2015.1073154
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发表时间：
2016
影响因子：
3.7
通讯作者：
Zheng H
中科院分区：
文献类型：
--
作者：
Das R;Banerjee M;Nan B;Zheng H
断棒模型中变点位置的估计在模拟重要的生物现象中具有重要的应用。在这篇文章中，我们提出了一个计算经济的基于似然的方法，有效地估计变点（S）在横截面和纵向设置。我们的方法，在每个变点的收缩邻域中的局部平滑的基础上，通过模拟被证明是计算上比现有的方法，依赖于搜索程序，具有显着的收益在多个变点的情况下，更可行。所提出的估计具有一致性和渐近正态性-特别是，他们是渐近有效的横截面设置-使我们能够提供有意义的统计推断。作为我们的主要和激励性（纵向）应用，我们研究了密歇根州骨骼健康和代谢研究队列数据，以描述最后一次月经前后雌二醇水平对数的变化模式，其中两个变点的断棒模型似乎是一个很好的拟合。我们还说明了我们的方法在植物生长数据集的横截面设置。
Estimation of change-point locations in the broken-stick model has significant applications in modeling important biological phenomena. In this article we present a computationally economical likelihood-based approach for estimating change-point(s) efficiently in both cross-sectional and longitudinal settings. Our method, based on local smoothing in a shrinking neighborhood of each change-point, is shown via simulations to be computationally more viable than existing methods that rely on search procedures, with dramatic gains in the multiple change-point case. The proposed estimates are shown to have -consistency and asymptotic normality – in particular, they are asymptotically efficient in the cross-sectional setting – allowing us to provide meaningful statistical inference. As our primary and motivating (longitudinal) application, we study the Michigan Bone Health and Metabolism Study cohort data to describe patterns of change in log estradiol levels, before and after the final menstrual period, for which a two change-point broken stick model appears to be a good fit. We also illustrate our method on a plant growth data set in the cross-sectional setting.