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

E. Skamnia, P. Economou, S. Bersimis, M. Frouda, A. Politis, P. Alexopoulos

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)2388-2407
Number of pages20
JournalJournal of Applied Statistics
Volume50
Issue number11-12
DOIs
StatePublished - 2023

Keywords

  • Andrews curves
  • COVID-19
  • Hot spot
  • dimensionality reduction
  • graphical representation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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