Monte Carlo Methods and Extreme Value Estimation

Arvid Naess
Additional contact information
Arvid Naess: Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering

Chapter Chapter 9 in Applied Extreme Value Statistics, 2024, pp 127-168 from Springer

Abstract: Abstract The last decade has seen a dramatic increase in the use of Monte Carlo methods for solving stochastic engineering problems. There are primarily two reasons for this increase. First, the computational power available today, even for a laptop computer, is formidable and steadily increasing. Second, the versatility of Monte Carlo methods make them very attractive as a way of obtaining solutions to stochastic problems. The drawback of Monte Carlo methods for a range of problems has been that the required numerical calculations may take days, weeks, or even months to do. But this situation is changing, some numerical problems that required several days of computer time for their solution just a few years ago can now be solved in minutes or hours. This has really opened the door for the use of Monte Carlo-based methods for solving a wide array of stochastic engineering problems. In this chapter the focus is on adapting Monte Carlo methods for estimation of extreme values of stochastic processes encountered in various engineering disciplines.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-60769-1_9

Ordering information: This item can be ordered from
http://www.springer.com/9783031607691

DOI: 10.1007/978-3-031-60769-1_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().