Gaussian Process Panel Modeling—Machine Learning Inspired Analysis of Longitudinal Panel Data

Frontiers in Psychology

article

Julian D. Karch¹  Andreas M. Brandmaier²  Manuel C. Voelkle⁴ 

[1] Institute of Psychology, Leiden University;Center for Lifespan Psychology, Max Planck Institute for Human Development;Max Planck UCL Centre for Computational Psychiatry and Ageing Research;Psychological Research Methods, Department of Psychology, Humboldt University of Berlin

关键词: longitudinal analysis;    machine learning;    statistical learning;    Bayesian;    continuous-time;    prediction;   

DOI  :  10.3389/fpsyg.2020.00351

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM) . 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.

【 授权许可】

CC BY