Recent advances in estimation techniques have underscored the growing importance of shrinkage estimation and balanced loss functions in the analysis of multivariate normal distributions. These ...
Duah: Using puzzles, both at home and in classrooms, can restore the often-forgotten truth that learning happens in ...
Researchers have proposed a unifying mathematical framework that helps explain why many successful multimodal AI systems work ...
Abstract: The imagery speech (IS) is the speech that the human beings are thinking in their brain. A brain computer interface (BCI) system is employed to translate the speech thinking in the brain to ...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each con stituent component is a Mat rn process. The model parameters are ...
Tito A. Mijares, The Moments of Elementary Symmetric Functions of the Roots of a Matrix in Multivariate Analysis, The Annals of Mathematical Statistics, Vol. 32, No. 4 (Dec., 1961), pp. 1152-1160 ...
TSD 20: Multivariate meta-analysis of summary data for combining treatment effects on correlated outcomes and evaluating surrogate endpoints (PDF, 1.2MB) – October 2019 – Updated December 2022: ...
Abstract: Multivariate time-series (MTS) anomaly detection is critical for industrial systems. However, for inter-variable dimensional modeling, existing work mainly focuses on low-order dependencies ...
Principal component analysis summarizes high dimensional data into a few dimensions. Each dimension is called a principal component and represents a linear combination of the variables. The first ...
A breakthrough deal to attempt to limit global temperature rises was agreed at a conference of world nations in December 2015. These charts from the time show how and why the Earth’s climate is ...
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