The marginalized likelihood ratio test for detecting abrupt changes

Gustafsson F.

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The marginalized likelihood ratio test for detecting abrupt changes

机译：用于检测突变的边缘化似然比检验

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The generalized likelihood ratio (GLR) test is a widely used method for detecting abrupt changes in linear systems and signals. In this paper the marginalized likelihood ratio (MLR) test is introduced for eliminating three shortcomings of GLR while preserving its applicability and generality. First, the need for a user-chosen threshold is eliminated in MLR. Second, the noise levels need not be known exactly and may even change over time, which means that MLR is robust. Finally, a very efficient exact implementation with linear in time complexity for batch-wise data processing is developed. This should be compared to the quadratic in time complexity of the exact GLR

机译：广义似然比（GLR）测试是一种广泛使用的检测线性系统和信号突变的方法。本文介绍了边缘化似然比（MLR）检验，以消除GLR的三个缺点，同时保留其适用性和通用性。首先，在MLR中消除了对用户选择阈值的需求。其次，噪声电平不必精确知道，甚至可以随时间变化，这意味着MLR十分可靠。最后，为批处理数据处理开发了一种非常有效的，具有线性复杂度的精确实现。这应该与精确GLR的二次方时间复杂度进行比较

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《IEEE Transactions on Automatic Control》 |1996年第1期|p.66-78|共13页
作者
Gustafsson F.;
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原文格式 PDF
正文语种 eng
中图分类自动化系统;
关键词
computational complexity; estimation theory; filtering theory; linear systems; parameter estimation; state-space methods; abrupt change detection; batch-wise data processing; generalized likelihood ratio; linear systems; marginalized likelihood ratio test; time com;

机译：计算复杂度;估计理论;滤波理论;线性系统;参数估计;状态空间方法;突变检测;逐批数据处理;广义似然比;线性系统;边际似然比检验;时间通信;

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专利

1. The marginalized likelihood ratio test for detecting abrupt changes [J] . Gustafsson F. IEEE Transactions on Automatic Control . 1996,第1期

机译：用于检测突变的边缘化似然比检验
2. Frequentist nonparametric goodness-of-fit tests via marginal likelihood ratios [J] . Hart Jeffrey D., Choi Taeryon, Yi Seongbaek Computational statistics & data analysis . 2016,第Null期

机译：通过边际似然比进行频繁的非参数拟合优度检验
3. Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method [J] . Cheng Cheng, Jean-Yves Tourneret, Quan Pan, Signal processing . 2016,第jana期

机译：使用基于边缘化似然比的方法检测，估计和校正影响GNSS信号的多径偏置
4. On the use of neural networks in the generalized likelihood ratio test for detecting abrupt changes in signals [C] . Fancourt, C.L., Principe, . 2000

机译：关于神经网络在广义似然比检验中用于检测信号突变的问题
5. Item Parameter Estimation Using Marginal Maximum Likelihood Estimation Under Targeted Testing Design [D] . Zhang, Ziwei. 2020

机译：项目参数估计使用目标测试设计下的边际最大似然估计
6. Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design [O] . Dylan Molenaar, Sophie van der Sluis, Dorret I. Boomsma, -1

机译：使用经典双胞胎设计中的边际最大似然估计通过环境相互作用检测特定基因型
7. The Marginalized Likelihood Ratio Test for Detecting Abrupt Changes [O] . Gustafsson, Fredrik 1994

机译：用于检测突变的边际似然比检验

The marginalized likelihood ratio test for detecting abrupt changes

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