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.

翻译：在本文中,我们根据纵向研究,通过射线内核功能,对时间变化系数模型的动态参数进行估计。我们的提案以带宽加权内核功能的线性组合为基础,以一组特定的时间点为中心。此外,我们研究不同的估计和推论备选办法,包括使用带靴方法的加权最小方和带子方法的常数法,以及通过Markov 链条Monte Carlo和变式方法的巴耶西亚法。我们通过分析两项艾滋病临床研究的数据,将上述估计战略与我们的射线内核功能提案进行对比,并以回归样条为基础,通过广泛的模拟研究,考虑从样本大小、重复测量次数和主题相关性等角度的多种假设。我们的实验表明,我们基于辐射内核功能的建议的能力确实与从回归样条中获取的数据相近甚至好。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。