Statistical Modelling with Applications

Statistical Modelling is a multidisciplinary science aiming to develop methods and techniques for the modelling of various phenomena in various scientific fields.

Since the early years of the statistical development the need for understanding the underlying mechanisms for the description and/or prediction a phenomenon, was immense. Since then, numerous research studies have been published that focus on various aspects of modelling covering goodness of fit tests, nonparametric statistics, biostatistics, time series, reliability theory, model selection criteria, divergence and information measures, multivariate analysis, etc. related among others to applications in biosciences, geosciences, financial and actuarial mathematics and technical systems. Additional experience has also been gathered from industrial, engineering, geophysical and medical settings. Thus, statistical modelling emerged quite early and remains today as one of main fields for scientists, researchers, medical experts, engineers, industrial managers etc.

This issue covers the recent developments in Statistical Modelling and presents new theoretical issues that were not previously presented in the literature, as well as the solutions of important practical problems and case studies illustrating the application methodology.

The issue is expected to be a collective work by a number of leading scientists, analysts, statisticians, mathematicians and engineers who have been working on the front end of statistical modelling. All manuscripts in the issue are going to be written by leading researchers and practitioners in their respective fields of expertise and present a plethora of innovative methods, approaches and solutions not covered before in the literature.