What are the Challenges in Using Covariance Matrices?
Despite their utility, there are several challenges associated with using covariance matrices in epidemiology:
Multicollinearity: High covariance between two or more predictor variables can lead to multicollinearity, which can distort the results of regression analyses. Sample Size: Reliable estimation of the covariance matrix requires a sufficiently large sample size. Small sample sizes can lead to unstable estimates. Non-linearity: Covariance captures only linear relationships, which means it can miss more complex, non-linear interactions between variables.
