By modeling the single-trial electroencephalogram of participants performing perceptual decisions, and building on predictions from two century-old psychological laws, we estimate the times of ...

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), ...

Professor Klaus Nordhausen develops modern multivariate statistical methods to analyze high-dimensional and large datasets in different fields.

Objective Patients with atrial fibrillation (AF) frequently have multiple comorbidities that increase the risk of hospitalisation and contribute to higher mortality. However, studies examining the ...

Background Despite anticoagulation, patients with atrial fibrillation (AF) experience persistent elevated cardiovascular risk ...

The researchers concluded from their analysis that “exhaled propanoic and nonanoic acids may serve as complementary non-invasive biomarkers for monitoring controlled asthma, independently of tobacco ...

Discover how multivariate models use multiple variables for investment forecasting, risk analysis, and decision-making in finance. Ideal for portfolio management.

Researchers have identified specific coupled patterns of brain activity and gene expression that help explain impulsive behavior in children with attention deficit hyperactivity disorder. By analyzing ...

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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 ...

Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Al-Hofuf, Saudi Arabia Alzheimer's disease (AD) is a devastating brain disorder that ...