ABSTRACT

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.

 抽象的

在本文中，我们在纵向研究的背景下通过径向核函数估计时变系数模型的动态参数。我们的建议基于涉及带宽的加权核函数的线性组合，以给定的时间点集为中心。此外，我们研究了估计和推理的不同替代方案，包括使用加权最小二乘和引导方法的频率方法，以及通过马尔可夫链蒙特卡罗和变分方法的贝叶斯方法。我们通过广泛的模拟研究，考虑样本大小、重复测量次数和特定主题等多个场景，对上述估计策略进行相互比较，以及我们的径向核函数建议与基于回归样条的扩展相关性。我们的实验表明，我们基于径向核函数的建议的能力确实可以与从回归样条获得的能力相媲美甚至更好。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。