Fr. 129.00

Compositional Data Analysis

English · Hardback

Will be released 31.12.2019

Description

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Compositional Data Analysis (CoDA) refers to the analysis of all those vectors representing parts of a whole which only carry relative information. This type of data is ubiquitous in most applications, especially in geology, chemistry, genetics, and environmental sciences. The last comprehensive treatment was written by John Aitchison in 1986, and this book represents an update of that classic book.


List of contents










Interesting problems with compositional data. Constraints and problems they cause. Distance in simplex sample space. Covariance structure. Logistic normal and other distributions on the simplex. Log ratio analysis (alr, clr, ilr). Dimension reduction. Bases and compositions. Subcompositions and partitions. Zeros, rounding, measurement error. Compositions as covariates and mixtures. Integer compositions, multi-way compositions, compositional processes. Appendix A: R software for compositions. Appendix B: Bayesian analysis for compositional data.


Summary

Compositional Data Analysis (CoDA) refers to the analysis of all those vectors representing parts of a whole which only carry relative information. This type of data is ubiquitous in most applications, but especially in geology, chemistry, genetics and environmental sciences. The last comprehensive treatment was written by John Aitchison in 1986, so this book would represent a sort of updat of that classic book. Key points:

  • Comprehensive coverage of compositional data analysis
  • Provides sound theory, but does not require high level mathematics
  • Motivated well by realistic problems
  • Self contained
  • Enables analysis of substantive real problems

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