Much health related research depends heavily on the analysis of a rapidly expanding universe of observational data. A challenge in analysis of such data is the lack of sound statistical methods and tools that can address ...

Biclustering is a technique for clustering rows and columns of a data matrix simultaneously. Over the past few years, we have seen its applications in biology-related fields, as well as in many data mining projects. As ...

A relatively recent increase in the popularity of evidence-based activism has created a higher demand for statisticians to work on human rights and economic development projects. The statistical challenges of revealing ...

Studies with stratified cluster designs, called complex surveys, have increased in popularity in medical research recently. With the passing of the Affordable Care Act, more information about effectiveness of treatment, ...

Studies are often performed in samples that do not resemble the target populations relevant for policy, treatment, or other decisions. Much of the causal inference literature has focused on addressing internal validity ...

Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal post-disaster resource allocation ...

Panel germline tests simultaneously detect multiple pathogenic variants, typically grouped by association with a higher risk of developing certain cancers or syndromes. Next-generation sequencing has made testing for many ...

Electronic health records (EHR) data has become crucial resources for a growing number of data-driven biomedical studies such as automated disease diagnosis and genotype-phenotype translation studies. Nevertheless, power ...

The manuscript discusses three topics that utilize optimal transport and related methodologies to solve problems in statistics. Chapter 2 uses the Wasserstein distance to construct interpretable approximations to complicated ...