Joshua Habiger

I earned a BS in Mathematics and MS in Statistics from Kansas State University in 2004 and 2006, respectively.  In 2010 earned a PhD in Statistics from the University of South Carolina and joined the Department of Statistics as an assistant professor.  I was promoted to associate professor in 2016 and promoted to professor in 2021.  

I enjoy developing new statistical theory and methods for the analysis of high dimensional (HD) data, especially those aimed at variable or model selection motivated by the analysis of HD count data arising in the "omics" or imaging. I find it particularly rewarding when the need for methodological development arises with collaborators, and results in new statistical methodology that is implementable broadly (ex. R package).  Recently I have become interested in replicability and meta analysis.  Here the goal is to assess and address replication and/or false discovery rates using, say, a collection of p-values or summary statistics that may be subject to selection or publication bias.  I also enjoy developing theory for p-value statistics with discrete distributions, such as mid-p-values, randomized p-values and natural p-values.  Here the goal is to develop theoretically sound p-values for downstream analysis, like multiple hypothesis testing or meta analysis. 

Keywords: False Discovery Rate; High Dimensional Data; Applications in "omics"; Categorical Data Analysis; Statistical Machine Learning Applications; Classification Analysis; Model Selection; Replicability Analysis; Multiple Hypothesis Testing; Model Selection