IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

QuantifQuantile; an R Package for Performing Quantile Regression through Optimal Quantization

Listed author(s):
  • Isabelle Charlier
  • Davy Paindaveine
  • Jérôme Saracco

Quantile regression allows to assess the impact of some covariate X on a response Y .An important application is the construction of reference curves and conditional predictionintervals for Y .Recently, Charlier et al. (2014a) developed a new nonparametric quantileregression method based on the concept of optimal quantization. This method, as shownin Charlier et al. (2014b), competes very well with its classical nearest-neighbor or kernelcompetitors. In this paper, we describe an R package, called QuantifQuantile, that allowsto perform quantization-based quantile regression. We describe the various functions of thepackage and provide examples.

If you experience problems downloading a file, check if you have the proper application to view it first. 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: https://dipot.ulb.ac.be/dspace/bitstream/2013/174930/1/2014-40-CHARLIER_PAINDAVEINE_SARACCO-quantifquantile.pdf
File Function: 2014-40-CHARLIER_PAINDAVEINE_SARACCO-quantifquantile
Download Restriction: no

Paper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number ECARES 2014-40.

as
in new window

Length: 20 p.
Date of creation: Sep 2014
Publication status: Published by:
Handle: RePEc:eca:wpaper:2013/174930
Contact details of provider: Postal:
Av. F.D., Roosevelt, 39, 1050 Bruxelles

Phone: (32 2) 650 30 75
Fax: (32 2) 650 44 75
Web page: http://difusion.ulb.ac.be

More information through EDIRC

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.:

as
in new window


  1. Isabelle Charlier & Davy Paindaveine, 2014. "Conditional Quantile Estimation through Optimal Quantization," Working Papers ECARES ECARES 2014-28, ULB -- Universite Libre de Bruxelles.
  2. Isabelle Charlier & Davy Paindaveine & Jérôme Saracco, 2014. "Conditional Quantile Estimation Based on Optimal Quantization: from Theory to Practice," Working Papers ECARES ECARES 2014-39, ULB -- Universite Libre de Bruxelles.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:eca:wpaper:2013/174930. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Benoit Pauwels)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.