Mitigating the effect of measurement errors in quantile estimation

E. Schechtman and C. Spiegelman

Statistics & Probability Letters, 2007, vol. 77, issue 5, 514-524

Abstract: Quantiles are frequently used as descriptive measures. When data contains measurement errors, using the contaminated data to estimate the quantiles results in biased estimates. In this paper, we suggest two methods for reducing the effect of measurement errors on the quantile estimates and compare them, via an extensive simulation study, to the estimates obtained by the naive method, that is: by the estimates obtained from the observed (contaminated) data. The method we recommend is based on a method in a paper by Cook and Stefanski. However, we suggest using a combination of bootstrap and jackknifing to replace their extrapolation step.

Keywords: Bootstrap; Jackknife; Percentiles (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(06)00272-0
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:stapro:v:77:y:2007:i:5:p:514-524

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().