Improving bias in kernel density estimation
Download full text from publisher
As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.
Other versions of this item:
References listed on IDEAS
- Lejeune, Michel & Sarda, Pascal, 1992. "Smooth estimators of distribution and density functions," Computational Statistics & Data Analysis, Elsevier, vol. 14(4), pages 457-471, November.
- Kairat Mynbaev & Carlos Martins-Filho, 2010.
"Bias reduction in kernel density estimation via Lipschitz condition,"
Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 219-235.
- Mynbaev, Kairat & Martins-Filho, Carlos, 2009. "Bias reduction in kernel density estimation via Lipschitz condition," MPRA Paper 24904, University Library of Munich, Germany.
- Christopher Withers & Saralees Nadarajah, 2013. "Density estimates of low bias," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(3), pages 357-379, April.
- Fan, Jianqing & Hu, Tien-Chung, 1992. "Bias correction and higher order kernel functions," Statistics & Probability Letters, Elsevier, vol. 13(3), pages 235-243, February.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Mynbaev, Kairat & Martins-Filho, Carlos, 2017.
"Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view,"
Statistics & Probability Letters, Elsevier, vol. 123(C), pages 17-22.
- Mynbaev, Kairat & Martins-Filho, Carlos, 2016. "Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view," MPRA Paper 75902, University Library of Munich, Germany, revised 2016.
More about this item
KeywordsDensity estimation; Bias; Higher order kernel;
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:94:y:2014:i:c:p:106-112. 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: (Haili He). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .
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 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 RePEc Author Service 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.