Abstract: Identifying causal drivers in multivariate time-series data is central to finance, climate science, and other domains where interactions are nonlinear, high-dimensional, and noisy. Standard ...
Abstract: Due to the characteristics of multi-dimensional, strong coupling and noise of complex industrial data, this paper establishes an energy efficiency recognition and diagnosis model using the ...
Rotated multivariate Linear Mixed Model (RmvLMM) is a powerful and scalable statistical framework for dual large-scale GWAS, applicable to biobank-scale samples and a large number of phenotypes.
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