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|a 10.1080/02664763.2021.2022607
|2 doi
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|a eng
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| 100 |
1 |
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|a Skamnia, E
|e verfasserin
|4 aut
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|a Hot spot identification method based on Andrews curves
|b an application on the COVID-19 crisis effects on caregiver distress in neurocognitive disorder
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 11.09.2023
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Informa UK Limited, trading as Taylor & Francis Group.
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|a Identifying and locating areas - hot spots - that present high concentration of observations in a high-dimensional data set is crucial in many data processing and analysis methods and techniques, since observations that belong to the same hot spot share information and behave in a similar way. A useful tool towards that aim is the reduction of the data dimensionality and the graphical representation of them. In the present paper, a new method to identify and locate hot spots is proposed, based on the Andrews curves. Simulations results demonstrate the performance of the proposed method, which is also applied to a high-dimensional data set, regarding caregiver distress related to symptoms of people with neurocognitive disorder and to the mental effects of the recent outbreak of the COVID-19 pandemic
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|a Journal Article
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|a Andrews curves
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|a COVID-19
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|a Hot spot
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| 650 |
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|a dimensionality reduction
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| 650 |
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4 |
|a graphical representation
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| 700 |
1 |
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|a Economou, P
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bersimis, S
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Frouda, M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Politis, A
|e verfasserin
|4 aut
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| 700 |
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|a Alexopoulos, P
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 50(2023), 11-12 vom: 09., Seite 2388-2407
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|g volume:50
|g year:2023
|g number:11-12
|g day:09
|g pages:2388-2407
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|u http://dx.doi.org/10.1080/02664763.2021.2022607
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