Density estimation for data with rounding errors

B. Wang and W. Wertelecki

Computational Statistics & Data Analysis, 2013, vol. 65, issue C, 4-12

Abstract: Rounding of data is common in practice. The problem of estimating the underlying density function based on data with rounding errors is addressed. A parametric maximum likelihood estimator and a nonparametric bootstrap kernel density estimator are proposed. Simulations indicate that the maximum likelihood approach performs well when prior information on the functional form of the underlying distribution is available, while the kernel-type estimator attains stable and good performance in various cases. The proposed methods are further applied to detect the distributional difference of head circumferences from two Chernobyl impacted regions of Ukraine.

Keywords: Bootstrapping; Kernel density estimation; Measurement error; Fast Fourier transformation; Deconvolution density estimation (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947312000989
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:65:y:2013:i:c:p:4-12

DOI: 10.1016/j.csda.2012.02.016

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 ().