An exploratory data analysis in scale-space for interval-valued data

Cheolwoo Park, Yongho Jeon, Kee Hoon Kang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


We propose an exploratory data analysis approach when data are observed as intervals in a nonparametric regression setting. The interval-valued data contain richer information than single-valued data in the sense that they provide both center and range information of the underlying structure. Conventionally, these two attributes have been studied separately as traditional tools can be readily used for single-valued data analysis. We propose a unified data analysis tool that attempts to capture the relationship between response and covariate by simultaneously accounting for variability present in the data. It utilizes a kernel smoothing approach, which is conducted in scale-space so that it considers a wide range of smoothing parameters rather than selecting an optimal value. It also visually summarizes the significance of trends in the data as a color map across multiple locations and scales. We demonstrate its effectiveness as an exploratory data analysis tool for interval-valued data using simulated and real examples.

Original languageEnglish
Pages (from-to)2643-2660
Number of pages18
JournalJournal of Applied Statistics
Issue number14
Publication statusPublished - 2016 Oct 25

Bibliographical note

Publisher Copyright:
© 2016 Taylor & Francis.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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