Java and Computing for Robust Statistics

V. Todorov ()
Additional contact information
V. Todorov: Ericsson Austria AG

A chapter in Developments in Robust Statistics, 2003, pp 404-416 from Springer

Abstract: Summary The recent advances in the Java technology provoked increasing interest in using Java as a programming language for development of numerical software. Given its attractive language features it worths investigating the possibilities of writing software for robust computing in Java. In this paper the design and implementation of an object-oriented library for computing of high breakdown point robust multivariate location and covariance matrix estimates called JMCD is presented and its performance as compared to the corresponding FORTRAN implementations is examined. The FAST-MCD algorithm of Rousseeuw and Van Driessen (1999) written in Java can be competitive to FORTRAN (on PC/Windows platforms). The Java implementation is on average 30% slower with its efficiency depending on the size of the problem, but this could be accepted given the other advantages of the language.

Keywords: Robust Statistics; Java Virtual Machine; Multivariate Location; Multidimensional Array; Minimum Covariance Determinant (search for similar items in EconPapers)
Date: 2003
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:978-3-642-57338-5_35

Ordering information: This item can be ordered from
http://www.springer.com/9783642573385

DOI: 10.1007/978-3-642-57338-5_35

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 ().