Hot spot identification method based on Andrews curves: an application on the COVID-19 crisis effects on caregiver distress in neurocognitive disorder

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 use...

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Bibliographic Details
Published in:Journal of applied statistics 2023-09, Vol.50 (11-12), p.2388-2407
Main Authors: Skamnia, E., Economou, P., Bersimis, S., Frouda, M., Politis, A., Alexopoulos, P.
Format: Article
Language:English
Subjects:
Andrews curves
Caregivers
COVID-19
Data processing
Datasets
Dementia
dimensionality reduction
graphical representation
Graphical representations
Hot spot
Identification methods
Neurological disorders
Statistical methods
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Summary: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.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2021.2022607