A Review and Some New Proposals for Bandwidth Selection in Nonparametric Density Estimation for Dependent Data

Inés Barbeito () and Ricardo Cao ()
Additional contact information
Inés Barbeito: Universidade da Coruña, Research Group MODES, CITIC, Faculty of Computer Science, Department of Mathematics
Ricardo Cao: Universidade da Coruña, Research Group MODES, CITIC, Faculty of Computer Science, Department of Mathematics

Chapter Chapter 10 in From Statistics to Mathematical Finance, 2017, pp 173-208 from Springer

Abstract: Abstract When assuming independence, the choice of the smoothing parameter in density estimation has been extensively studied. However, when considering dependence, very few papers have dealt with data-driven bandwidth selectors. First of all, we review the state of art of the existing methods for bandwidth selection under dependence. Moreover, three new (or adapted) methods are proposed: (a) an extension to the dependent case of the modified cross-validation by Stute (J Statisti Plann Infer 30:293–305, 1992; b) an adaptation to density estimation of the penalized cross-validation proposed by Estévez-Pérez et al. (J Statisti Plann Infer 104:1–30, 2002) for hazard rate estimation; (c) finally, the smoothed version of the so-called moving blocks bootstrap is established and an exact expression for the bootstrap version of the mean integrated squared error under dependence is obtained in this context. This is useful since Monte Carlo approximation is not needed to implement the bootstrap selector. An extensive simulation study is carried out in order to check and compare the empirical behaviour of six selected bandwidths.

Keywords: Bandwidth choice; Bootstrap bandwidth; Comparison by simulation; Cross-validation bandwidth; Kernel density estimator; Plug-in bandwidth; Moving blocks bootstrap; Modified cross-validation; Penalized cross-validation (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_10

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

DOI: 10.1007/978-3-319-50986-0_10

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 ().