Covariance modelling

Understanding interactions between objects, typically evolving through time.

Coding examples using R programming language.

Duration

5 to 10 h

Syllabus

Covariance estimation:

• Definition and properties
• Estimating sample covariance matrix
• Alternative estimation strategies

Factor models

• Principal Component Analysis
• Factor Models
• Inference
• Selecting number of factors

Network models

• Graphs and Networks definition
• Partial Correlation Networks
• Lasso Estimation Partial Correlation Network
• Implementation Issues

Prerequisites

Basic knowledge of R coding.

Credential

Taught in executive (ad-hoc) training courses, and also in MSc Data Science.