We propose fast and scalable statistical methods for the analysis of hundreds or thousands of high dimensional vectors observed at multiple visits. The proposed inferential methods avoid the difficult task of loading the entire data set at once in the computer memory and use sequential access to data. This allows deployment of our methodology on low-resource computers where computations can be done in minutes on extremely large data sets. Our methods are motivated by and applied to a study where hundreds of subjects were scanned using Magnetic Resonance Imaging (MRI) at two visits roughly five years apart. The original data possesses over ten billion easurements. The approach can be applied to any type of study where data can be unfolded into a long vector including densely bserved functions and images.
Statistical Methodology | Statistical Theory
Zipunnikov, Vadim; Caffo, Brian; Crainiceanu, Ciprian; Yousem, David M.; Davatzikos, Christos; and Schwartz, Brian S., "MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR HIGH-DIMENSIONAL DATA" (October 2010). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 219.