Stochastic Modeling and Statistical Methods: Advances and Applications

· ·
· Academic Press
Llibre electrònic
350
Pàgines
Apte
Aquest llibre estarà disponible el dia 1 de juny del 2025. No se t'aplicarà cap càrrec fins que no es publiqui.

Sobre aquest llibre

Stochastic Modeling and Statistical Methods: Advances and Applications is the practical guide to the latest developments in data analysis and research methods. The book explores the significant research progress that has been seen in recent decades, offering vital tools for analyzing modern applications and real data. Topics covered include Dynamic Reliability, Stochastic Modeling, System Maintainability, and Parametric, Semi-Parametric, and Nonparametric Statistical Inference. Readers will find the latest advancements in these areas, making it an essential resource for researchers and practitioners who want to explore these evolving fields and stay updated on cutting-edge research. - Presents the latest breakthroughs in Reliability Engineering, along with current perspectives on the field - Includes shared, practical knowledge of contemporary statistical modeling techniques, thus enhancing analytical skills - Covers the probabilistic methods used to investigate various applications in Reliability Engineering

Sobre l'autor

Ioannis S. Triantafyllou is an Assistant Professor at the Department of Statistics and Insurance Science of the University of Piraeus. His research interests focus on the area of Statistical Reliability Theory and Nonparametric Statistical Quality Control. More than 60 research papers of him in the field of Applied Probability and Statistics have been published in international scientific journals and edited volumes. In the literature, there exist more than 500 citations of his research work, while he has served as a referee for 35 international scientific journals. He serves as an Associate Editor for International Journal of Mathematical Engineering and Management Sciences (indexed by Web of Science) and as a Reviewer for Mathematical Reviews/MathSciNet (by American Mathematical Society). He has served as Guest Editor for special issues published in two scientific journals indexed by Web of Science and Scopus.

Sonia Malefaki is an Assistant Professor at the Department of Mechanical Engineering & Aeronautics of the University of Patras. Her research interests are mainly in computational statistics, simulation methods, Monte Carlo and Markov chain Monte Carlo methods. She works also on Bayesian Statistics, Markov and semi Markov processes, hidden Markov/semi Markov models, reliability and maintenance. She is the author of 36 papers in international journals and edited volumes and more than 15 papers in international conference proceedings with referees. In the literature, there exist 698 citations (google scholar) of her research work, while he has served as a referee for 16 international scientific journals. She has served as Guest Editor on the special issue on Reliability and Stochastic Processes of the Journal of Reliability and Statistical Studies JRSS.

Alex Karagrigoriou is a Professor of Probability and Statistics at the Department of Statistics and Actuarial-Financial Mathematics of the University of the Aegean. His research activities cover various areas of Statistics such as Applied Probability, Mathematical Statistics, Statistical Modeling, Time Series Analysis, Goodness of fit tests, Markov and semi-Markov Processes, Stochastic Modelling, Statistical Quality Control and Reliability Theory. He has published more than 100 articles (journals & collective volumes), edited or co-edited 15 collective volumes & special issues and has given more than 80 invited presentations in international conferences, and universities all over the world. His work has received (according to Scopus) more than 600 citations (h-index=12). His ORCID id is 0000-0002-4919-2133. Finally, he has been involved in the organization of conferences, workshops and summer schools and has great experience in the design and execution of research projects with external funding which involve statistical analysis of (bio)medical and (socio)economic data.

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