We discuss issues of structural and practical identifiability of partially observed differential
equations which are often applied in systems biology. The development of mathematical …

Often researchers find parametric models restrictive and sensitive to deviations from the
parametric specifications; semi-nonparametric models are more flexible and robust, but lead …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

ABSTRACT A key challenge for modern Bayesian statistics is how to perform scalable
inference of posterior distributions. To address this challenge, variational Bayes (VB) …

BACKGROUND Two large‐scale prostate cancer screening trials using prostate‐specific
antigen (PSA) have given conflicting results in terms of the efficacy of such screening. One of …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

This package includes both Mathematical Statistics: Basic Ideas and Selected Topics,
Volume I, Second Edition, as well as Mathematical Statistics: Basic Ideas and Selected …

Motivation: Mathematical description of biological reaction networks by differential equations
leads to large models whose parameters are calibrated in order to optimally explain …

We introduce a statistical model for microarray gene expression data that comprises data
calibration, the quantification of differential expression, and the quantification of …

Empirical likelihood provides inferences whose validity does not depend on specifying a
parametric model for the data. Because it uses a likelihood, the method has certain inherent …