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

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Listed:
  • E. Skamnia
  • P. Economou
  • S. Bersimis
  • M. Frouda
  • A. Politis
  • P. Alexopoulos

Abstract

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.

Suggested Citation

  • E. Skamnia & P. Economou & S. Bersimis & M. Frouda & A. Politis & P. Alexopoulos, 2023. "Hot spot identification method based on Andrews curves: an application on the COVID-19 crisis effects on caregiver distress in neurocognitive disorder," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(11-12), pages 2388-2407, September.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2388-2407
    DOI: 10.1080/02664763.2021.2022607
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