In almost all fields of applications, statisticians are confronted with highly complex data sets with large dimensions. Dimension reduction methods naturally provide a better understanding of the data and reveal hidden structures. The book provides tools with a unifying statistical theory to recover hidden structures, latent variables, or latent subspaces in multivariate and dependent data. Throughout the book, the theory is illustrated with examples on practical data sets.
Publisher: John Wiley & Sons Inc
Number of pages: 420
You may also be interested in...
Would you like to proceed to the App store to download the Waterstones App?