固定时间区间上方差齐性均值模型的断点检测
A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals
摘要：
这项工作的动机是应用程序的GNSS衍生IWV（综合水汽）系列。实际上，这些GPS系列会受到由于设备变化或干扰效应而引起的突然变化的影响。从这些变化中检测和校正系列是用于气候研究之前的关键步骤。除了这些突然的变化之外，在该系列中还观察到一种非平稳的变异性。本文提出了一种新的分割模型，即在已知时间间隔上具有非均匀方差的高斯过程的均值模型中进行断点检测。在这种分割情况下，传统的用于推断断点的动态规划（DP）算法不能再应用。我们提出了一个程序，在两个步骤：我们首先估计稳健的方差，然后通过插入这些估计应用经典的推断。我们所提出的程序的性能进行评估，通过模拟实验。实际GNSS数据的应用。
英文摘要：
This work is motivated by an application for the homogeneization of GNSS-derived IWV (Integrated Water Vapour) series. Indeed, these GPS series are affected by abrupt changes due to equipment changes or environemental effects. The detection and correction of the series from these changes is a crucial step before any use for climate studies. In addition to these abrupt changes, it has been observed in the series a non-stationary of the variability. We propose in this paper a new segmentation model that is a breakpoint detection in the mean model of a Gaussian process with heterogeneous variance on known time-intervals. In this segmentation case, the dynamic programming (DP) algorithm used classically to infer the breakpoints can not be applied anymore. We propose a procedure in two steps: we first estimate robustly the variances and then apply the classical inference by plugging these estimators. The performance of our proposed procedure is assessed through simulation experiments. An application to real GNSS data is presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
