Statistical genetics is undergoing the same transition to big data that all branches of applied
statistics are experiencing, and this transition is only accelerating with the advent of …

Modern computational statistics is turning more and more to high‐dimensional optimization
to handle the deluge of big data. Once a model is formulated, its parameters can be …

The problem of minimizing a continuously differentiable convex function over an intersection
of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when …

We investigate a robust penalized logistic regression algorithm based on a minimum
distance criterion. Influential outliers are often associated with the explosion of parameter …

Recent studies have demonstrated theoretical attractiveness of a class of concave penalties
in variable selection, including the smoothly clipped absolute deviation and minimax …

J. Darroch and D. Ratliff. Generalized iterative scaling for log-linear models, Annals of Math.
Stat., 43: 1470-1480, 1972. D. Bohning and B. Lindsay. Monotonicity of quadratic approxima …

This dissertation shows that careful injection of noise into sample data can substantially
speed up Expectation-Maximization algorithms. Expectation-Maximization algorithms are a …

Abstract Machine learning is a field of science aiming to extract knowledge from the data.
Optimization lies in the core of machine learning as many learning problems are formulated …

In this work, we explore a distance association method, Logistic Gifi, for categorical data
which advances geometric data analysis techniques in much the same way that …

Finite mixtures of distributions have provided a mathematical‐based approach to the
statistical modeling of a wide variety of random phenomena. They underpin a variety of …