Abstract: Multivariate statistical analysis is the use of mathematical models and statistical theory to study multivariate problems. Based on Bloom's cognitive theory, according to the learner's ...

The AERCA algorithm performs robust root cause analysis in multivariate time series data by leveraging Granger causal discovery methods. This implementation in PyTorch facilitates experimentation on ...

Quarterback Aidan O'Connell suffered a fractured wrist in the Raiders' preseason finale Saturday according to coach Pete Carroll. Carroll went on to say the backup quarterback will need surgery and is ...

Recent advances in green chemistry have made multivariate experimental design popular in sample preparation development. This approach helps reduce the number of measurements and data for evaluation ...

Abstract: The existing literature on forecasting time series data is primarily based on univariate analysis and techniques such as Univariate Autoregressive (UAR), Univariate Moving Average (UMA), ...

Genetic diversity is important for conservation and genetic improvement of common beans. This study aimed to estimate the genetic diversity among common bean genotypes from the Embrapa germplasm ...

With single-gene based drug target identification methods getting saturated, the scientific world is venturing beyond traditional approaches to enhance early drug discovery. We explore how combining ...

Survival outcomes for metastatic castration sensitive prostate cancer: Real world data from a Danish cohort 2016-2022.