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Proficiency tests, Evaluating
descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Other literature type 2017 Netherlands Publisher:Wiley
Authors: Cofino, W.P.; Molenaar, J.; Torfs, P.J.J.F.;
doi: 10.1002/9781118445112.stat04068.pub2
doi: 10.1002/9781118445112.stat04068.pub2
Marine monitoring programs provide data that are essential for marine management. The reliability of such data is underpinned by proficiency tests. In the context of Quasimeme, a proficiency testing program for the marine environment, a statistical model has been developed in 2000 to evaluate data sets. The mathematical basis of this model has been reformulated in 2016. In this article, the new mathematical basis is concisely explained. The main results of a comparative study using five statistical procedures on about 2400 data sets are described. Examples of the application of the five techniques on two data sets with complicated distributions are given. It is concluded that the NDA implementation of our model offers the highest robustness and that a thorough evaluation should invoke graphical representations of the data sets and consider robust skewness and results from different statistical methods.
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Part of book or chapter of book . 2017
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Research@WUR
Other literature type . 2017
Data sources: Research@WUR
https://onlinelibrary.wiley.co...
Other literature type
Data sources: Microsoft Academic Graph
Wiley
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Research@WUR
Part of book or chapter of book . 2017
Data sources: Research@WUR
Research@WUR
Other literature type . 2017
Data sources: Research@WUR
https://onlinelibrary.wiley.co...
Other literature type
Data sources: Microsoft Academic Graph
Wiley
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shareshareShare
citeCite
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Please grant OpenAIRE to access and update your ORCID works.
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The following results are related to Energy Research. Are you interested to view more results? Visit OpenAIRE - Explore.

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Proficiency tests, Evaluating
descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Other literature type 2017 Netherlands Publisher:Wiley
Authors: Cofino, W.P.; Molenaar, J.; Torfs, P.J.J.F.;
doi: 10.1002/9781118445112.stat04068.pub2
doi: 10.1002/9781118445112.stat04068.pub2
Marine monitoring programs provide data that are essential for marine management. The reliability of such data is underpinned by proficiency tests. In the context of Quasimeme, a proficiency testing program for the marine environment, a statistical model has been developed in 2000 to evaluate data sets. The mathematical basis of this model has been reformulated in 2016. In this article, the new mathematical basis is concisely explained. The main results of a comparative study using five statistical procedures on about 2400 data sets are described. Examples of the application of the five techniques on two data sets with complicated distributions are given. It is concluded that the NDA implementation of our model offers the highest robustness and that a thorough evaluation should invoke graphical representations of the data sets and consider robust skewness and results from different statistical methods.
Research@WURarrow_drop_down
Research@WUR
Part of book or chapter of book . 2017
Data sources: Research@WUR
Research@WUR
Other literature type . 2017
Data sources: Research@WUR
https://onlinelibrary.wiley.co...
Other literature type
Data sources: Microsoft Academic Graph
Wiley
linkLink to
shareshareShare
citeCite
addClaim
Please grant OpenAIRE to access and update your ORCID works.
This Research product is the result of merged Research products in OpenAIRE.

You have already added works in your ORCID record related to the merged Research product.
This Research product is the result of merged Research products in OpenAIRE.

You have already added works in your ORCID record related to the merged Research product.
Select content type to embed
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For further information contact us at helpdesk@openaire.eu
0
citations 0
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Powered by BIP!
more_vert
Research@WURarrow_drop_down
Research@WUR
Part of book or chapter of book . 2017
Data sources: Research@WUR
Research@WUR
Other literature type . 2017
Data sources: Research@WUR
https://onlinelibrary.wiley.co...
Other literature type
Data sources: Microsoft Academic Graph
Wiley
linkLink to
shareshareShare
citeCite
addClaim
Please grant OpenAIRE to access and update your ORCID works.
This Research product is the result of merged Research products in OpenAIRE.

You have already added works in your ORCID record related to the merged Research product.
This Research product is the result of merged Research products in OpenAIRE.

You have already added works in your ORCID record related to the merged Research product.
Select content type to embed
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