Nonparametric density estimation from biased data with unknown biasing function

Lloyd, Chris J. and Jones, M. C. (2000). Nonparametric density estimation from biased data with unknown biasing function. Journal of the American Statistical Association, 95(451) pp. 865–876.

URL:	http://www.jstor.org/stable/2669470
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Abstract

We present a kernel estimator for the density of a variable when sampling probabilities depend on that variable. Both the density and sampling bias weight functions are unknown and are estimated nonparametrically. To achieve this, the method requires that two independent samples be taken from a fixed finite population. An estimator of population size follows simply from our density estimator. Asymptotic bias and standard errors for these estimators are provided, and the methodology is illustrated both on simulation data and on a dual-list dataset of aboriginal people in the Vancouver-Richmond area of Canada.

Item Type:	Journal Item
Copyright Holders:	2000 American Statistical Association
ISSN:	1537-274X
Keywords:	kernel density estimation; mark recapture; weighted distribution
Academic Unit/School:	Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID:	23864
Depositing User:	Sarah Frain
Date Deposited:	05 Apr 2011 14:21
Last Modified:	02 May 2018 13:17
URI:	http://oro.open.ac.uk/id/eprint/23864
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