Nonparametric density estimation from biased data with unknown biasing function

• 成果类型: Article
• 署名作者: Lloyd, CJ; Jones, MC
• 署名单位: Open University - UK
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2669470
• 发表日期: 2000
• 页码: 865-876
• 关键词: recapture experiments; population; size

摘要

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.