Böcker av R. Dennis (The University of Minnesota Cook

Written by the leading expert in the field, this text reviews the major new developments in envelope models and methods  An Introduction to Envelopes provides an overview of the theory and methods of envelopes, a class of procedures for increasing efficiency in multivariate analyses without altering traditional objectives. The author offers a balance between foundations and methodology by integrating illustrative examples that show how envelopes can be used in practice. He discusses how to use envelopes to target selected coefficients and explores predictor envelopes and their connection with partial least squares regression. The book reveals the potential for envelope methodology to improve estimation of a multivariate mean. The text also includes information on how envelopes can be used in generalized linear models, regressions with a matrixΓÇôvalued response, and reviews work on sparse and Bayesian response envelopes. In addition, the text explores relationships between envelopes and other dimension reduction methods, including canonical correlations, reducedΓÇôrank regression, supervised singular value decomposition, sufficient dimension reduction, principal components, and principal fitted components. This important resource:  ΓÇó    Offers a text written by the leading expert in this field ΓÇó    Describes groundbreaking work that puts the focus on this burgeoning area of study ΓÇó    Covers the important new developments in the field and highlights the most important directions ΓÇó    Discusses the underlying mathematics and linear algebra ΓÇó    Includes an online companion site with both R and Matlab support Written for researchers and graduate students in multivariate analysis and dimension reduction, as well as practitioners interested in statistical methodology, An Introduction to Envelopes offers the first book on the theory and methods of envelopes.

Simple graphs have always played a useful role in the analysis and presentation of data. Advances in computing have created the potential to substantially expand the role of visualization in statistical analyses. This book is about ideas for the graphical analysis of regression data.