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

Cited 3 time in webofscience Cited 0 time in scopus
  • Hit : 64
  • Download : 0
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.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2016-11
Language
English
Article Type
Article
Citation

JOURNAL OF APPLIED STATISTICS, v.43, no.14, pp.2643 - 2660

ISSN
0266-4763
DOI
10.1080/02664763.2016.1142947
URI
http://hdl.handle.net/10203/285754
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 3 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0