Data Management and Analysis Core (DMAC)

Our Center encompasses research from many different disciplines, all requiring specialized statistical methods. In some cases, we need entirely new methods in order to better understand and model complex relationships. We also need a data repository so that others may easily access the tools and databases that we are developing. The DMAC provides this kind of support across all projects.

Our Goals

The goal of the DMAC is to provide statistical, bioinformatics, and data management support for all aspects of the project.

Our Approach

Our team is known for developing innovative statistical methods to help solve questions around environmental exposures and to integrate high dimensional exposure, molecular, and phenotypic data. We also provide support in geographic information systems (GIS) and statistics education and training for trainees and researchers connected with the SRC, and engage pre- and post-doctoral trainees from the Biostatistics Department in SRC projects.





Recent Publications

Nilanjana Laha, Nathan Huey, Brent Coull, and Rajarshi Mukherjee. 9/24/2021. “On Statistical Inference with High Dimensional Sparse CCA.” Publisher's VersionAbstract
We consider asymptotically exact inference on the leading canonical correlation directions and strengths between two high dimensional vectors under sparsity restrictions. In this regard, our main contribution is the development of a loss function, based on which, one can operationalize a one-step bias-correction on reasonable initial estimators. Our analytic results in this regard are adaptive over suitable structural restrictions of the high dimensional nuisance parameters, which, in this set-up, correspond to the covariance matrices of the variables of interest. We further supplement the theoretical guarantees behind our procedures with extensive numerical studies.