Diagnostic procedures for finding outliers in high dimensional multivariate time series and
robust estimation methods for these data are reviewed. First, methods for searching for …

Consider a spatial blind source separation model in which the observed multivariate spatial
data are assumed to be a linear mixture of latent stationary spatially uncorrelated random …

The direpack package establishes a set of modern statistical dimensionality reduction
techniques into the Python universe as a single, consistent package. Several of the methods …

It has been argued that hunter-gatherers' food-sharing may have provided the basis for a
whole range of social interactions, and hence its study may provide important insight into the …

This chapter presents an introduction to robust statistics with applications of a chemometric
nature. Following a description of the basic ideas and concepts behind robust statistics …

Modern industrial machines can generate gigabytes of data in seconds, frequently pushing
the boundaries of available computing power. Together with the time criticality of industrial …

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities
in bridge health monitoring data. Testing is of great significance. For abnormal data …

We introduce a class of regularized M-estimators of multivariate scatter and show,
analogous to the popular spatial sign covariance matrix (SSCM), that they possess high …

针对米波(VHF) 雷达的复杂多径信号中散射分量的非高斯性严重影响测高的稳定性,
该文提出了稳健的空域符号变换最大似然测高算法. 该算法先对多维阵列快拍矢量进行空域符号 …

Robust alternatives exist for many statistical estimators. State-of-the-art robust methods are
fine-tuned to optimize the balance between statistical efficiency and robustness. The …