Estimating the number of signals using principal component analysis
Joni Virta, Klaus Nordhausen · Stat · 2019
In this work, we develop inferential tools for determining the correct number of principal components under a general noisy latent variable model, which includes as a special case, for example, the noisy independent component model. The problem is approached using hypothesis testing, and we provide both a large‐sample test and several resampling‐based alternatives. Simulations and an application to sound data reveal that both types of approaches keep the desired levels and have good power.