Finding structure in multivariate time series
Abstract
The scope of scientific data collection in modern projects such as the human genome
project has made it effectively impossible for careful by-hand analyses of such data to be
carried out. Simultaneously, the increase in computer power raises the possibility of replacing
human scrutiny with computer systems that could effectively sort and filter copious data,
presenting only the most salient features to researchers. This thesis details a method for
combining a generalized version of the classical statistical method known as canonical correlation
analysis, that possesses good computational properties, with the more recently developed
multitaper spectral estimators. The developed method allows researchers to combine
data from multiple experiments to generate more accurate spectral decompositions of the
underlying processes involved while also giving researchers a sensitive method for finding the
links between variables in the data sets. The only limitation is that the data to be analyzed
must be homogeneous in certain specific ways (for example, it must contain no pronounced
trends).
URI
http://purl.galileo.usg.edu/uga_etd/praissman_jeremy_l_200712_mahttp://hdl.handle.net/10724/24470