This book is devoted to a new branch of experimental design theory called simulation experimental design. There are many books devoted either to the theory of experimental design or to system simulation techniques, but in this book an approach to combine both fields is developed. Especially the mathematical theory of such universal variance reduction techniques as splitting and Russian Roulette is explored. The book contains a number of results on regression design theory related to nonlinear problems, the E-optimum criterion and designs which minimize bias. Audience: This volume will be of value to readers interested in systems simulation, applied statistics and numerical methods with basic knowledge of applied statistics and linear algebra.

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.

This volume features original contributions and invited review articles on mathematical statistics, statistical simulation and experimental design. The selected peer-reviewed contributions originate from the 8th International Workshop on Simulation held in Vienna in 2015. The book is intended for mathematical statisticians, Ph.D. students and statisticians working in medicine, engineering, pharmacy, psychology, agriculture and other related fields. The International Workshops on Simulation are devoted to statistical techniques in stochastic simulation, data collection, design of scientific experiments and studies representing broad areas of interest. The first 6 workshops took place in St. Petersburg, Russia, in 1994 - 2009 and the 7th workshop was held in Rimini, Italy, in 2013.

This book is devoted to a new branch of experimental design theory called simulation experimental design. There are many books devoted either to the theory of experimental design or to system simulation techniques, but in this book an approach to combine both fields is developed. Especially the mathematical theory of such universal variance reduction techniques as splitting and Russian Roulette is explored. The book contains a number of results on regression design theory related to nonlinear problems, the E-optimum criterion and designs which minimize bias. Audience: This volume will be of value to readers interested in systems simulation, applied statistics and numerical methods with basic knowledge of applied statistics and linear algebra.