Computationally Intensive Techniques
Wolfgang Karl Härdle () and
Leopold Simar
Additional contact information
Wolfgang Karl Härdle: Humboldt-Universität zu Berlin, Ladislaus von Bortkiewicz Chair of Statistics
Chapter Chapter 20 in Applied Multivariate Statistical Analysis, 2019, pp 487-539 from Springer
Abstract:
Abstract It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This exposure to computational methods is of an essential nature when we consider extremely high-dimensional data. Computer-aided techniques can help us to discover dependencies in high dimensions without complicated mathematical tools.
Date: 2019
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
Chapter: Computationally Intensive Techniques (2024)
Chapter: Computationally Intensive Techniques (2015)
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:978-3-030-26006-4_20
Ordering information: This item can be ordered from
http://www.springer.com/9783030260064
DOI: 10.1007/978-3-030-26006-4_20
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 ().