The faculty and staff have extensive experience in many statistical areas including:

  • experimental design

  • sample size selection

  • analysis of microarray data

  • analysis of variance (ANOVA)

  • Bayesian methods

  • bioinformatics

  • cluster analysis

  • conjoint analysis

  • deep neural modeling

  • dimension reduction

  • functional data

  • generalized linear models

  • graphical models

  • logistic regression

  • longitudinal studies

  • machine learning

  • mixed effect models

  • multivariate analysis

  • nonlinear modeling

  • randomized clinical trials

  • regression

  • semi-parametric and non-parametric inference

  • spatial and spatio-temporal data

  • structural equation modeling

  • survey sampling

  • survival analysis

Our statistical methodology expertise also has experience working with various types of dependent data including:

  • categorical data

  • clinical trial data

  • complex survey data

  • experimental data

  • functional data

  • longitudinal data

  • multivariate data

  • network data

  • panel data

  • spatial and spatio-temporal data

  • time series data

  • data with missingness