Statistical Data Modeling and Machine Learning with Applications

Building models from observed data and making sense of it has long been central to statistics. As computing power has grown, that work now also includes uncovering useful and often concealed patterns in data collections of very different sizes, along with intricate datasets stored in warehouses. Statistical techniques combined with machine learning and data mining approaches have been developed to meet these demands.

All of these methods rest on a demanding mathematical foundation that draws on probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods and related fields. Widely used machine learning tools, including artificial neural networks, support vector machines, decision trees and random forests, produce models that rely in a fairly direct way on optimization theory and statistical estimation. Pairing established statistical methods with these tools makes it possible to tackle many demanding practical problems.

This Special Issue appears in the Mathematics and Computer Science section. It brings together a compact selection of carefully chosen papers that offer new and original methods, data analyses, case studies, comparative evaluations and other research on statistical data modeling and machine learning and their applications. Although not restricted to them, the contributions give particular attention to fields such as computer science, medicine, engineering, banking, education, sociology and economics. Together, the methods, algorithms and applications presented here aim to advance research in this area.

The collection is intended for young scientists, doctoral students and researchers from a range of scientific disciplines.