Doğu and Kocakoc (2013) estimated the step change point of multivariate normal processes when joint mean vector and covariance matrix shifts occurred.
Doğu and Kocakoc (2011) estimated the time of step change in the covariance matrix of multivariate normal processes, in which a multivariate control chart based on sample covariance is used for receiving out-of-control signals.
To assess the performances of the different methods in detection of associations in a similar-sized dataset, we simulated 500 datasets with 500 individuals and 300 covariates, sampled from a multivariate normal distribution (the simulation process is detailed in Additional file 1).
Then, this information is converted into a forecast via an autoregressive process and a multivariate normal mixture model (Everitt, 1993; Chatfield, 2004).
is generated with various complexity: different sizes, hidden component structures, correlation structures and even the presence of multicolinearity (simulated from a multivariate normal with a first-order autoregressive process's covariance matrix with auto-correlation ).
Since the number of samples is usually limited in this training set, a model is chosen beforehand: The generation of this set is modeled as drawing samples from a random process P x), where the distribution of this random process is approximated by a multivariate normal distribution N μ, Σ.
