oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Design-based and Model-assisted estimators using Machine learning methods: Exploring the k-Nearest Neighbor metod applied to data from the Recreational Fishing Survey
Örebro University, Örebro University School of Business.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Place, publisher, year, edition, pages
2019. , p. 21
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:oru:diva-72488OAI: oai:DiVA.org:oru-72488DiVA, id: diva2:1289045
Subject / course
Statistik
Supervisors
Available from: 2019-02-15 Created: 2019-02-15 Last updated: 2019-03-21Bibliographically approved

Open Access in DiVA

fulltext(1649 kB)100 downloads
File information
File name FULLTEXT02.pdfFile size 1649 kBChecksum SHA-512
307d8e594bd536db79f08da574a680729b1da894f124faf5bb5ed3b33c93e8ac46ef498306cd6a7e951280eef8d91625dcc50ef712c032f3e11dff8c387ecea5
Type fulltextMimetype application/pdf

By organisation
Örebro University School of Business
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 103 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 383 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf