Gaussian Process Panel Modeling-Machine Learning Inspired Analysis of Longitudinal Panel Data.

ABSTRACT: 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

SUBMITTER: Karch JD 

PROVIDER: S-EPMC7096578 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature