Nonparametric density estimation of streaming data using orthogonal series

Kyle A. Caudle and Edward Wegman

Computational Statistics & Data Analysis, 2009, vol. 53, issue 12, 3980-3986

Abstract: Streaming data represent a serious challenge because implicit in the nature of streaming data, data are not exchangeable and are not storable. This means data must be processed on the fly. Density estimation is an essential tool used to make sense of data collected by large scale systems. In this paper, we present a recursive method for constructing and updating an estimate of the nonstationary probability density function. Our approach is shown to work well with simulated data as well as with real data.

Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00239-4
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:53:y:2009:i:12:p:3980-3986

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().