Kader—An R Package for Nonparametric Kernel Adjusted Density Estimation and Regression

Gerrit Eichner ()
Additional contact information
Gerrit Eichner: Justus-Liebig-University Giessen, Mathematical Institute

Chapter Chapter 15 in From Statistics to Mathematical Finance, 2017, pp 291-315 from Springer

Abstract: Abstract In a series of three papers published from 2011 through 2013, Stute and coauthors introduced a fully data-adaptive nonparametric kernel method for pointwise univariate density estimation and likewise for regression estimation. For density estimation a robustified version of this adaptive method was also provided and the pointwise method was extended to an $$L_2$$ -approach. Here, an R package is presented that implements (so far) parts of those methods. This package is a first attempt to narrow the gap between the theoretical derivation of the methods and their availability for practical applications.

Keywords: Pointwise Method; Kernel Density Estimator; Rank Transformation; Appraisal Bias; Real Data Situation (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-50986-0_15

Ordering information: This item can be ordered from
http://www.springer.com/9783319509860

DOI: 10.1007/978-3-319-50986-0_15

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().