Confidence bands in non‐parametric errors‐in‐variables regression
Confidence bands in non‐parametric errors‐in‐variables regression
复制标题
非参数变量误差回归中的置信带
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
F. Jamshidi
中科院分区：
文献类型：
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作者：
A. Delaigle;P. Hall;F. Jamshidi
变量误差回归在科学和社会科学的许多领域都很重要，例如在经济学中，它通常是享乐模型的一个特征，在环境科学中，空气质量指数被错误测量，在生物学中，植物的营养质量经常被错误测量所掩盖，在营养学中，报告的脂肪摄入量通常会出现重大错误。到目前为止，在非参数背景下，绝大多数工作都集中在估计作为函数的均值的方法上，相对较少关注估计量准确性的经验评估技术。我们开发了构建置信带的方法。我们的贡献包括技术调整参数的选择，旨在最大限度地减少覆盖误差的置信带。
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.