This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Rainbow plots, Bagplots and Boxplots for Functional Data

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Rob J. Hyndman ()
Han Lin Shang ()

Additional information is available for the following registered author(s):

Abstract

We propose new tools for visualizing large numbers of functional data in the form of smooth curves or surfaces. The proposed tools include functional versions of the bagplot and boxplot, and make use of the first two robust principal component scores, Tukey's data depth and highest density regions. By-products of our graphical displays are outlier detection methods for functional data. We compare these new outlier detection methods with exiting methods for detecting outliers in functional data and show that our methods are better able to identify the outliers.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2008/wp9-08.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 9/08.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 26 pages
Date of creation: Nov 2008
Date of revision:
Handle: RePEc:msh:ebswps:2008-9

Contact details of provider:
Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Phone: +61-3-9905-2489
Fax: +61-3-9905-5474
Email:
Web page: http://www.buseco.monash.edu.au/depts/ebs/
More information through EDIRC

Order Information:
Email:
Web: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/

For technical questions regarding this item, or to correct its listing, contact: (Simone Grose).

Related research
Keywords: Highest density regions; Robust principal component analysis; Kernel density estimation; Outlier detection; Tukey's halfspace depth;

Other versions of this item:

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Tarn Duong & Martin L. Hazelton, 2005. "Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association and Swedish Statistical Association, vol. 32(3), pages 485-506. [Downloadable!] (restricted)
  2. Reiss, Philip T. & Ogden, R. Todd, 2007. "Functional Principal Component Regression and Functional Partial Least Squares," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 984-996, September. [Downloadable!] (restricted)
  3. Sara Lopez-Pintado & Juan Romo, 2006. "On The Concept Of Depth For Functional Data," Statistics and Econometrics Working Papers ws063012, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  4. Struyf, Anja & Rousseeuw, Peter J., 2000. "High-dimensional computation of the deepest location," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 415-426, October. [Downloadable!] (restricted)
  5. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June. [Downloadable!] (restricted)
    Other versions:
  6. Becker, Claudia & Gather, Ursula, 2001. "The largest nonidentifiable outlier: a comparison of multivariate simultaneous outlier identification rules," Computational Statistics & Data Analysis, Elsevier, vol. 36(1), pages 119-127, March. [Downloadable!] (restricted)
  7. Hyde, Valerie & Jank, Wolfgang & Shmueli, Galit, 2006. "Investigating Concurrency in Online Auctions Through Visualization," The American Statistician, American Statistical Association, vol. 60, pages 241-250, August. [Downloadable!] (restricted)
  8. Filzmoser, Peter & Maronna, Ricardo & Werner, Mark, 2008. "Outlier identification in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1694-1711, January. [Downloadable!] (restricted)
  9. Croux, Christophe & Ruiz-Gazen, Anne, 2005. "High breakdown estimators for principal components: the projection-pursuit approach revisited," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 206-226, July. [Downloadable!] (restricted)
  10. Manuel Febrero & Pedro Galeano & Wenceslao González-Manteiga, 2007. "A functional analysis of NOx levels: location and scale estimation and outlier detection," Computational Statistics, Springer, vol. 22(3), pages 411-427, September. [Downloadable!] (restricted)
  11. Kargin, V. & Onatski, A., 2008. "Curve forecasting by functional autoregression," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2508-2526, November. [Downloadable!] (restricted)
    Other versions:
Full references

Statistics
Access and download statistics

Did you know? RePEc also has a blog.

This page was last updated on 2009-11-25.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.