论文标题
各向异性局部恒定平滑平滑，以进行更改点回归函数估计
Anisotropic local constant smoothing for change-point regression function estimation
论文作者
论文摘要
了解加拿大任何地区的森林火灾蔓延对于促进森林健康和保护人类的生活和基础设施至关重要。量化火势从嘈杂的图像中传播的，在噪音的区域被变更点边界隔开，这对于忠实估算火灾传播速度至关重要。在这项研究中，我们开发了一个统计上一致的平滑估计器，使我们能够从微火实验中降低火灾传播图像。我们为更改点数据开发了一种各向异性平滑方法，该方法使用基础数据生成过程的估计来告知平滑。我们表明，各向异性本地常数回归估计器与收敛速率$ o \ left（n^{ - 1/{（q+2）}}} \ right）$一致。我们证明了其对模拟的一维更改点数据的有效性，并从微火实验中传播了图像。
Understanding forest fire spread in any region of Canada is critical to promoting forest health, and protecting human life and infrastructure. Quantifying fire spread from noisy images, where regions of a fire are separated by change-point boundaries, is critical to faithfully estimating fire spread rates. In this research, we develop a statistically consistent smooth estimator that allows us to denoise fire spread imagery from micro-fire experiments. We develop an anisotropic smoothing method for change-point data that uses estimates of the underlying data generating process to inform smoothing. We show that the anisotropic local constant regression estimator is consistent with convergence rate $O\left(n^{-1/{(q+2)}}\right)$. We demonstrate its effectiveness on simulated one- and two-dimensional change-point data and fire spread imagery from micro-fire experiments.
