Estimating the Minimal Value of a Function in Global Random Search: Comparison of Estimation Procedures

Emily Hamilton, Vippal Savani and Anatoly Zhigljavsky ()
Additional contact information
Emily Hamilton: Cardiff University
Vippal Savani: Cardiff University
Anatoly Zhigljavsky: Cardiff University

A chapter in Models and Algorithms for Global Optimization, 2007, pp 193-214 from Springer

Abstract: Summary In a variety of global random search methods, the minimum of a function is estimated using either one of linear estimators or the the maximum likelihood estimator. The asymptotic mean square errors (MSE) of several linear estimators asymptotically coincide with the asymptotic MSE of the maximum likelihood estimator. In this chapter we consider the non-asymptotic behaviour of different estimators. In particular, we demonstrate that the MSE of the best linear estimator is superior to the MSE of the the maximum likelihood estimator.

Date: 2007
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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:spochp:978-0-387-36721-7_13

Ordering information: This item can be ordered from
http://www.springer.com/9780387367217

DOI: 10.1007/978-0-387-36721-7_13

Access Statistics for this chapter

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