Statistics Department

Spatially dependent data are common in environmental and climate studies, where Gaussian process models are used for prediction and uncertainty quantification. Learning their covariance structure involves both statistical and computational challenges. Likelihood-based inference may be sensitive to covariance misspecification, while repeated evaluation of prediction-oriented criteria can be computationally demanding. To study these issues, we develop […]

Multivariate dependent discrete data routinely arise in microbiome studies. Analyzing these data presents interesting statistical challenges, such as high dimensionality, excess zeros, large heterogeneity across samples, and temporal dependence in […]