Sparse principal component analysis (SPCA) extends classical principal component analysis to settings where the number of variables greatly exceeds the number of observations. By imposing sparsity ...

As a multivariate statistical method, the Principal component analysis has been applied to many research fields. Recently, a seismological study successfully introduced the Principal component ...

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in ...