Vera Pawlowsky-Glahn – författare

In the summer of 1970, Georges Matheron, the father of geostatistics, presented a series of lectures at the Centre de Morphologie Mathmatique in France. These lectures would go on to become Matheron''s Theory of Regionalized Variables, a seminal work that would inspire hundreds of papers and become the bedrock of numerous theses and books on the topic; however, despite their importance, the notes were never formally published.In this volume, Matheron''s influential work is presented as a published book for the first time. Originally translated into English by Charles Huijbregts, and carefully curated here, this book stays faithful to Matheron''s original notes. The text has been ordered with a common structure, and equations and figures have been redrawn and numbered sequentially for ease of reference.While not containing any mathematical technicalities or case studies, the reader is invited to wonder about the physical meaning of the notions Matheron deals with. When Matheron wrote them, he considered the theory of linear geostatistics complete and the book his final one on the subject; however, this end for Matheron has been the starting point for most geostatisticians.

'Geostatistical Analysis of Compositional Data' provides a comprehensive coverage of the theory and practice of analysis of data that have both spatial and compositional dependence, characteristics of most earth science and environmental measurements.

In the summer of 1970, Georges Matheron, the father of geostatistics, presented a series of lectures at the Centre de Morphologie Mathmatique in France. These lectures would go on to become Matheron's Theory of Regionalized Variables, a seminal work that would inspire hundreds of papers and become the bedrock of numerous theses and books on the topic; however, despite their importance, the notes were never formally published. In this volume, Matheron's influential work is presented as a published book for the first time. Originally translated into English by Charles Huijbregts, and carefully curated here, this book stays faithful to Matheron's original notes. The text has been ordered with a common structure, and equations and figures have been redrawn and numbered sequentially for ease of reference.While not containing any mathematical technicalities or case studies, the reader is invited to wonder about the physical meaning of the notions Matheron deals with. When Matheron wrote them, he considered the theory of linear geostatistics complete and the book his final one on the subject; however, this end for Matheron has been the starting point for most geostatisticians.

It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology. This book presents the history and development of compositional data analysis along with Aitchison's log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science. Key Features: Reflects the state-of-the-art in compositional data analysis.Gives an overview of the historical development of compositional data analysis, as well as basic concepts and procedures.Looks at advances in algebra and calculus on the simplex.Presents applications in different fields of science, including, genomics, ecology, biology, geochemistry, planetology, chemistry and economics.Explores connections to correspondence analysis and the Dirichlet distribution.Presents a summary of three available software packages for compositional data analysis.Supported by an accompanying website featuring R code.Applied scientists working on compositional data analysis in any field of science, both in academia and professionals will benefit from this book, along with graduate students in any field of science working with compositional data.

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, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.

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, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.

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, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.

It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'''', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology.

This book presents the history and development of compositional data analysis along with Aitchison''s log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science.

Key Features:

Reflects the state-of-the-art in compositional data analysis. Gives an overview of the historical development of compositional data analysis, as well as basic concepts and procedures. Looks at advances in algebra and calculus on the simplex. Presents applications in different fields of science, including, genomics, ecology, biology, geochemistry, planetology, chemistry and economics. Explores connections to correspondence analysis and the Dirichlet distribution. Presents a summary of three available software packages for compositional data analysis. Supported by an accompanying website featuring R code.

Applied scientists working on compositional data analysis in any field of science, both in academia and professionals will benefit from this book, along with graduate students in any field of science working with compositional data.