基于径向基函数的时变系数模型估计
Time-Varying Coefficient Model Estimation Through Radial Basis Functions
摘要：
在本文中，我们估计的动态参数的时变系数模型通过径向核函数的纵向研究的背景下。我们的建议是基于一个线性组合的加权核函数涉及的带宽，集中在一组给定的时间点。此外，我们研究了不同的替代估计和推断，包括使用加权最小二乘法与自举方法的频率主义方法，和贝叶斯方法，通过马尔可夫链蒙特卡罗和变分方法。我们比较了上述的估计策略，我们的径向核函数的建议与扩展的基础上回归样条，通过广泛的模拟研究，考虑多个场景的样本量，重复测量的数量，和特定主题的相关性。我们的实验表明，我们的建议的能力的基础上径向核函数确实是可比的，甚至比那些从回归样条。我们通过分析两项艾滋病临床研究的数据来说明我们的方法。
英文摘要：
In this paper we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation and inference including a Frequentist approach using weighted least squares along with bootstrap methods, and a Bayesian approach through both Markov chain Monte Carlo and variational methods. We compare the estimation strategies mention above with each other, and our radial kernel functions proposal with an expansion based on regression spline, by means of an extensive simulation study considering multiples scenarios in terms of sample size, number of repeated measurements, and subject-specific correlation. Our experiments show that the capabilities of our proposal based on radial kernel functions are indeed comparable with or even better than those obtained from regression splines. We illustrate our methodology by analyzing data from two AIDS clinical studies.
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