Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications

This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications. The refereed contributions originate from the 10th International Workshop on Simulation and Statistics, SimStat 2019, which was held in Salzburg, Austria, September 2-6, 2019, and were either presented at the conference or developed afterwards, relating closely to the topics of the workshop. The book is intended for statisticians and Ph.D. students who seek current developments and applications in the field.

Auteur

Jürgen Pilz is Professor Emeritus at the Department of Statistics at the Alpen-Adria University Klagenfurt in Austria. His research areas include Bayesian statistics, spatial statistics, environmental and industrial statistics, statistical quality control and design of experiments.Viatcheslav B. Melas is a Professor at the Department of Stochastic Simulation at the St. Petersburg State University, Russia. His research areas include experimental design, stochastic simulation and regression analysis, with a focus on functional approaches to optimal experimental design.

Arne Bathke is Full Professor of Statistics at the Paris Lodron University Salzburg, Austria. His main research interests are related to nonparametric and multivariate statistics applied in different fields, from social sciences to biomedicine and engineering.