Wavelet-based functional mixed model analysis: Computational considerations

Richard C. Herrick, The University of Texas M.D. Anderson Cancer Center
Jeffrey S. Morris, The University of Texas M.D. Anderson Cancer Center

Abstract

Wavelet-based Functional Mixed Models is a new Bayesian method extending mixed models to irregular functional data (Morris and Carroll, JRSS-B, 2006). These data sets are typically very large and can quickly run into memory and time constraints unless these issues are carefully dealt with in the software. We reduce runtime by 1.) identifying and optimizing hotspots, 2.) using wavelet compression to do less computation with minimal impact on results, and 3.) dividing the code into multiple executables to be run in parallel using a grid computing resource. We discuss rules of thumb for estimating memory requirements and computation times in terms of model and data set parameters. We present examples and benchmarks demonstrating that it is practical to analyze very large data sets with readily available computing resources. This code is freely available on our website.