The investigator and his collaborators and thesis advisees develop diagnostic methods for a variety of problems linked by their use of data having structure beyond that of a univariate random sample. Examples include the identification of outliers and unexpected parameter changes in multiple regression, multivariate time series data, and large rectangular data arrays. Application of these methodologies to statistical quality improvement resolves many deficiencies of current approaches and provides high performance charting methods.
Data sets keep growing in size and complexity. Proteomics and microarray experiments routine provide data sets of a size scarcely imaginable a decade ago. This creates a need for sturdy statistical methods to extract information without manual checking and in the face of a fraction of erroneous or missing readings. The research addresses these needs, by developing appropriate statistical methods and effective ways of implementing them. At the other end of the size spectrum, just-in-time and the ability to customize in manufacturing create a need for statistical quality control tools able to react quickly, and to relatively small departures from specification. Change-point methodologies provide sharp tools for this detection and diagnosis.
