About

Complexity and Statistics Research

The Complexity and Statistics Research site features reviews of statistics-related publications with an emphasis on the foundations of statistics and on the interface with information theory. It also announces research results of the Complexity and Statistics Research Lab.

The lab

At the Complexity and Statistics Research Lab, we seek ways to assess complex information relevant to health care, renewable energy, and other applications. Improved statistical methods of weighing evidence enable more reliable interpretations of both case-control measurements of genomes and experimental measurements of transcript, protein, and metabolite levels in the cell. A more thorough understanding of these data impacts biomedicine and biotechnology.

Systems biology applications

We are improving statistical methods of weighing evidence to enable more reliable interpretations of biological complexity manifest in (1) experimental measurements of transcript, protein, and metabolite levels in the cell and (2) case-control measurements of genomes. In the first application, we have introduced statistical methods for the analysis of gene expression microarray data and other functional genomics data. The methods include the creation and testing of new ways to estimate levels of gene expression. In the second application of this research program, we have extended similar methods developed for gene expression data to genome-wide association studies.

More information

For details on the research summarized above, see the lab’s publications and preprints. See also David Bickel’s older publications. No content of davidbickel.com is written in the name or on behalf of the University of Ottawa.