Person:
Moradi, Mohammad Mehdi

Profile Picture

Email Address

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/49607

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Moradi

First Name

Mohammad Mehdi

person.page.departamento

Estadística, Informática y Matemáticas

person.page.instituteName

ORCID

0000-0003-3905-4498

person.page.upna

811779

Name

Filters

20231
Reset filters

Settings

Has files: true × Subject: Clustering ×

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    PublicationOpen Access
    Hierarchical spatio-temporal change-point detection
    (Taylor and Francis Group, 2023) Moradi, Mohammad Mehdi; Cronie, Ottmar; Pérez Goya, Unai; Mateu, Jorge; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent dependencies between different marginals and the latter may suffer from domination/masking among different change-points of distinct marginals. As a remedy, we propose an approach which groups marginals with similar temporal behaviors, and then performs group-wise multivariate change-point detection. Our approach groups marginals based on hierarchical clustering using distances which adjust for inherent dependencies. Through a simulation study we show that our approach, by preventing domination/masking, significantly enhances the general performance of the employed multivariate change-point detection method. Finally, we apply our approach to two datasets: (i) Land Surface Temperature in Spain, during the years 2000–2021, and (ii) The WikiLeaks Afghan War Diary data.