Written by the renowned expert in the field, this textbook reviews the major new developments in envelope models and methods
An Introduction to Envelopes: Dimension Reduction for Efficient Estimation in Multivariate Statistics (PDF) provides an outline of the theory and methods of envelopes, a class of procedures for increasing efficiency in multivariate analyses without changing traditional objectives. The author provides a balance between foundations and methodology by combining illustrative examples that show how envelopes can be used in practice. He explains how to use envelopes to target selected coefficients and examines predictor envelopes and their connection with partial least squares regression. The ebook reveals the potential for envelope methodology to improve the estimation of a multivariate mean.
The textbook also has information on how envelopes can be used in generalized linear models, regressions with a matrix-valued response, and reviews work on sparse and Bayesian response envelopes. Moreover, the textbook explores relationships between envelopes and other dimension reduction methods, including canonical correlations, reduced-rank regression, supervised singular value decomposition, sufficient dimension reduction, principal components, and principal fitted components. This important resource:
• Offers a textbook written by the leading expert in this field
• Discusses the underlying mathematics and linear algebra
• Includes an online companion site with both R and Matlab support
• Describes groundbreaking work that puts the focus on this burgeoning area of study
• Covers the important new developments in the field and highlights the most important directions
Written for graduate students and researchers in multivariate analysis and dimension reduction, and also practitioners interested in statistical methodology, An Introduction to Envelopes offers the first ebook on the theory and methods of envelopes.
NOTE: The product includes the ebook, An Introduction to Envelopes in PDF. No access codes are included.