Modeling and Analysis of Compositional Data (Statistics in Practice)

Hardcover, xvii + 247 pages, NOT ex-library. A clean and bright copy with unmarked text and firm binding, free of inscriptions and stamps. Boards show gentle external wear. Published without a dust jacket. -- Modeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method. Based upon short courses delivered by the authors, it provides a complete and current compendium of fundamental to advanced methodologies along with exercises at the end of each chapter to improve understanding, as well as data and a solutions manual which is available on an accompanying website. Complementing Pawlowsky-Glahn's earlier collective text that provides an overview of the state-of-the-art in this field, the book fills a gap in the literature for a much-needed manual for teaching, self learning or consulting. -- Contents: 1 Introduction 2 Compositional Data and Their Sample Space [Basic concepts; Principles of compositional analysis; Zeros, missing values, and other irregular components; Exercises] 3 The Aitchison Geometry [General comments; Vector space structure; Inner product, norm and distance; Geometric figures; Exercises] 4 Coordinate Representation [Introduction; Compositional observations in real space; Generating systems; Orthonormal coordinates; Balances; Working on coordinates; Additive logratio coordinates (alr); Orthogonal projections; Matrix operations in the simplex; Coordinates leading to alternative Euclidean structures; Exercises] 5 Exploratory Data Analysis [General remarks; Sample center, total variance, and variation matrix; Centering and scaling; Biplot: a graphical display; Exploratory analysis of coordinates; A geological example; Linear trends along principal components; A nutrition example; A political example; Exercises] 6 Random Compositions [Sample space; Variability and center; Probability distributions on the simplex; Exercises] 7 Statistical Inference [Point estimation of center and variability; Testing hypotheses on compositional normality; Testing hypotheses about two populations; Probability and confidence regions for normal data; Bayesian estimation with count data; Exercises] 8 Linear Models [Linear regression with compositional response; Regression with compositional covariates; Analysis of variance with compositional response; Linear discrimination with compositional predictor; Exercises] 9 Compositional Processes [Linear processes; Mixture processes; Settling processes; Simplicial derivative; Elementary differential equations; Exercises] 10 Epilogue; References; Appendix A Practical Recipes [Plotting a ternary diagram; Parameterization of an elliptic region; Matrix expressions of change of representation]; App B Random Variables [Probability spaces and random variables; Description of probability]; Author & General Index.…