التفاصيل البيبلوغرافية
| العنوان: |
Association measures for interval variables |
| المؤلفون: |
M. Rosário Oliveira, Margarida Azeitona, António Pacheco, Rui Valadas |
| المصدر: |
Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), Advances in Data Analysis and Classification. 16(3):491-520 |
| سنة النشر: |
2022 |
| الوصف: |
Symbolic Data Analysis (SDA) is a relatively new field of statistics that extends conventional data analysis by taking into account intrinsic data variability and structure. Unlike conventional data analysis, in SDA the features characterizing the data can be multi-valued, such as intervals or histograms. SDA has been mainly approached from a sampling perspective. In this work, we propose a model that links the micro-data and macro-data of interval-valued symbolic variables, which takes a populational perspective. Using this model, we derive the micro-data assumptions underlying the various definitions of symbolic covariance matrices proposed in the literature, and show that these assumptions can be too restrictive, raising applicability concerns. We analyze the various definitions using worked examples and four datasets. Our results show that the existence/absence of correlations in the macro-data may not be correctly captured by the definitions of symbolic covariance matrices and that, in real data, there can be a strong divergence between these definitions. Thus, in order to select the most appropriate definition, one must have some knowledge about the micro-data structure. |
| نوع الوثيقة: |
redif-article |
| اللغة: |
English |
| DOI: |
10.1007/s11634-021-00445 |
| الإتاحة: |
https://ideas.repec.org/a/spr/advdac/v16y2022i3d10.1007_s11634-021-00445-8.html |
| رقم الأكسشن: |
edsrep.a.spr.advdac.v16y2022i3d10.1007.s11634.021.00445.8 |
| قاعدة البيانات: |
RePEc |
