Karch Julian D, Brandmaier Andreas M, Voelkle Manuel C
Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, Netherlands.
Formal Methods in Lifespan Psychology, Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
Front Psychol. 2020 Mar 19;11:351. doi: 10.3389/fpsyg.2020.00351. eCollection 2020.
In this article, we extend the Bayesian nonparametric regression method to the analysis of longitudinal panel data. We call this new approach . GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes the approach exact and fast. GPPMs are defined using the kernel-language, which can express many traditional modeling approaches for longitudinal data, such as linear structural equation models, multilevel models, or state-space models but also various commonly used machine learning approaches. As a result, GPPM is uniquely able to represent hybrid models combining traditional parametric longitudinal models and nonparametric machine learning models. In the present paper, we introduce GPPM and illustrate its utility through theoretical arguments as well as simulated and empirical data.
在本文中，我们将贝叶斯非参数回归方法扩展到纵向面板数据的分析中。我们将这种新方法称为广义预测过程模型（GPPM）。由于GPPM能够表示大量模型，因此它具有很大的灵活性。它既允许经典统计推断，也支持受机器学习启发的预测建模。GPPM提供了频率主义和贝叶斯推断，而无需借助基于马尔可夫链蒙特卡罗的近似方法，这使得该方法既精确又快速。GPPM是使用核语言定义的，它可以表达许多用于纵向数据的传统建模方法，如线性结构方程模型、多层模型或状态空间模型，同时也能表达各种常用的机器学习方法。因此，GPPM能够独特地表示结合传统参数化纵向模型和非参数机器学习模型的混合模型。在本文中，我们介绍了GPPM，并通过理论论证以及模拟和实证数据来说明其效用。
