Showing posts from March, 2016

Using random effects models in prediction problems

by NICHOLAS A. JOHNSON, ALAN ZHAO, KAI YANG, SHENG WU, FRANK O. KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. Finally, through a case study of a real-world prediction problem, we also argue that Random Effect models should be considered alongside penalized GLM's even for pure prediction problems. Random effects models are a useful tool for both exploratory analyses and prediction problems. We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. In prediction problems these models can summarize the variation in the response, and in the process produce a form of ada