Confidence bands in non-parametric errorsin-variables regression

• 成果类型: Article
• 署名作者: Delaigle, Aurore; Hall, Peter; Jamshidi, Farshid
• 署名单位: University of Melbourne
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12067
• 发表日期: 2015
• 页码: 149-169
• 关键词: in-variables; deconvolution problems; DENSITY-ESTIMATION; Wild Bootstrap; Optimal Rates; CONVERGENCE; intervals; coverage; Iteration; models

摘要

Errors-in-variables regression is important in many areas of science and social science, e.g. in economics where it is often a feature of hedonic models, in environmental science where air quality indices are measured with error, in biology where the vegetative mass of plants is frequently obscured by mismeasurement and in nutrition where reported fat intake is typically subject to substantial error. To date, in non-parametric contexts, the great majority of work has focused on methods for estimating the mean as a function, with relatively little attention being paid to techniques for empirical assessment of the accuracy of the estimator. We develop methodologies for constructing confidence bands. Our contributions include techniques for tuning parameter choice aimed at minimizing the coverage error of confidence bands.