Asymptotic confidence sets for the jump curve in bivariate regression problems

Asymptotic confidence sets for the jump curve in bivariate regression problems
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二元回归问题中跳跃曲线的渐近置信集

DOI:
10.1016/j.jmva.2019.02.017
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发表时间:
2019
期刊:
J. Multivar. Anal.
影响因子:
--
通讯作者:
Holzmann
Holzmann
中科院分区:
--
文献类型:
--
作者:
Viktor;Eulert;Matthias;Holzmann

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我们为平滑图像函数中的单个边缘构造均匀且逐点渐近置信集,该函数基于两个单侧核估计器的旋转差异。使用 M 估计方法,我们展示了边缘函数的位置、斜率和高度估计器的一致性,并开发了对比度过程的均匀线性化。然后,均匀置信带依赖于评分过程的高斯近似以及高斯过程至上的反集中结果,而逐点带则基于渐近正态性。在模拟研究中研究了所提出的逐点方法的有限样本性能。还给出了现实世界图像处理的说明。
We construct uniform and point-wise asymptotic confidence sets for the single edge in an otherwise smooth image function which are based on rotated differences of two one-sided kernel estimators. Using methods from M-estimation, we show consistency of the estimators of location, slope and height of the edge function and develop a uniform linearization of the contrast process. The uniform confidence bands then rely on a Gaussian approximation of the score process together with anti-concentration results for suprema of Gaussian processes, while point-wise bands are based on asymptotic normality. The finite-sample performance of the point-wise proposed methods is investigated in a simulation study. An illustration to real-world image processing is also given.
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