Regularized boxplot via convex clustering

Cited 1 time in webofscience Cited 0 time in scopus
  • Hit : 59
  • Download : 0
A boxplot is a simple and effective exploratory data analysis tool for graphically summarizing a distribution of data. However, in cases where the quartiles in a boxplot are inaccurately estimated, these estimates can affect subsequent analyses. In this paper, we consider the problem of constructing boxplots in a bivariate setting with a categorical covariate with multiple subgroups, and assume that some of these boxplots can be clustered. We propose to use this grouping property to improve the estimation of the quartiles. We demonstrate that the proposed method more accurately estimates the quartiles compared to the usual boxplot. It is also shown that the proposed method identifies outliers effectively as a consequence of accurate quartiles, and possesses a clustering effect due to the group property. We then apply the proposed method to annual maximum precipitation data in South Korea and present its clustering results.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2019-05
Language
English
Article Type
Article
Citation

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.89, no.7, pp.1227 - 1247

ISSN
0094-9655
DOI
10.1080/00949655.2019.1576045
URI
http://hdl.handle.net/10203/285770
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 1 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0