Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers. From their early applications to current ...

Genome-Wide Association Studies (GWAS) have transformed human genetics by identifying thousands of loci associated with complex traits and diseases. Yet, individual GWAS are often underpowered, and ...

Abstract: Entropy serves as an effective nonlinear dynamic indicator of time series complexity. A number of multivariate entropy methods exist and are effectively used in signal analysis. Existing ...

The goal of this talk is to familiarize those in attendance with some common multivariate methods, such as principal component analysis, factor analysis, Hotelling’s T 2, etc. We’ll try to motivate ...

Realized volatility analysis of assets in the Brazilian market within a multivariate framework is the focus of this study. Despite the success of volatility models in univariate scenarios, challenges ...

Our research group develops modern and efficient multivariate statistical methods tailored for different types of multivariate data, such as time series, spatial data, spatio-temporal data, or ...

This course is available on the MPhil/PhD in Environmental Economics, MPhil/PhD in International Relations, MPhil/PhD in Management - Information Systems and Innovation, MPhil/PhD in Social Policy, ...