Applied Compositional Data Analysis
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Applied Compositional Data Analysis
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Author : Peter Filzmoser
language : en
Publisher: Springer
Release Date : 2018-11-03
Applied Compositional Data Analysis written by Peter Filzmoser and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-03 with Mathematics categories.
This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.
Advances In Compositional Data Analysis
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Author : Peter Filzmoser
language : en
Publisher: Springer Nature
Release Date : 2021-06-01
Advances In Compositional Data Analysis written by Peter Filzmoser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-01 with Mathematics categories.
This book presents modern methods and real-world applications of compositional data analysis. It covers a wide variety of topics, ranging from an updated presentation of basic concepts and ideas in compositional data analysis to recent advances in the context of complex data structures. Further, it illustrates real-world applications in numerous scientific disciplines and includes references to the latest software solutions available for compositional data analysis, thus providing a valuable and up-to-date guide for researchers and practitioners working with compositional data. Featuring selected contributions by leading experts in the field, the book is dedicated to Vera Pawlowsky-Glahn on the occasion of her 70th birthday.
Innovations In Multivariate Statistical Modeling
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Author : Andriëtte Bekker
language : en
Publisher: Springer Nature
Release Date : 2022-12-15
Innovations In Multivariate Statistical Modeling written by Andriëtte Bekker and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-15 with Mathematics categories.
Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics. It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty. Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.
Compositional Data Analysis
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Author : Vera Pawlowsky-Glahn
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-19
Compositional Data Analysis written by Vera Pawlowsky-Glahn and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-19 with Mathematics categories.
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.
Sedentary Behaviour Epidemiology
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Author : Michael F. Leitzmann
language : en
Publisher: Springer Nature
Release Date : 2023-12-23
Sedentary Behaviour Epidemiology written by Michael F. Leitzmann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-23 with Medical categories.
This book addresses the origins, determinants and magnitude of the global problem of sedentary behaviour, along with concise yet in-depth solutions for tackling it. As a consequence of major technological advances in modern society, many people find themselves in environments characterized by prolonged sedentary behaviour. Building on the contributions of leading experts in the field, the new edition of this book presents updated knowledge about sedentary behaviour, its medical and public health significance, its correlates and determinants, measurement techniques, and recommendations for addressing this behaviour at the individual, community, environmental, and policy level. The book encompasses current research linking the COVID-19 pandemic to increased levels of sedentary behavior, and it covers global and planetary health aspects of sedentary behavior, highlighting sustainable development goals such as health and well-being for all. Applying a cross-disciplinarymethodology, the book avoids considering physical activity and sedentary behavior as a single continuum, which potentially hampers progress in confronting widespread levels of sedentariness. Rather, the book helps readers better understand how sedentary and physically active behavior co-occur and how the two behaviours have distinct contributing factors. Building on the contributions of distinguished international experts in the field, this thorough resource is a valuable asset and challenges professionals, researchers, students, and practitioners alike to adopt new strategies and expand their reach.
Compositional Data Analysis In The Geosciences
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Author : Antonella Buccianti
language : en
Publisher: Geological Society of London
Release Date : 2006
Compositional Data Analysis In The Geosciences written by Antonella Buccianti and has been published by Geological Society of London this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Mathematics categories.
Since Karl Pearson wrote his paper on spurious correlation in 1897, a lot has been said about the statistical analysis of compositional data, mainly by geologists such as Felix Chayes. The solution appeared in the 1980s, when John Aitchison proposed to use Iogratios. Since then, the approach has seen a great expansion, mainly building on the idea of the `natural geometry' of the sample space. Statistics is expected to give sense to our perception of the natural scale of the data, and this is made possible for compositional data using Iogratios. This publication will be a milestone in this process.
Data Science For The Geosciences
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Author : Lijing Wang
language : en
Publisher: Cambridge University Press
Release Date : 2023-08-17
Data Science For The Geosciences written by Lijing Wang and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-17 with Science categories.
Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.
Encyclopedia Of Mathematical Geosciences
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Author : B. S. Daya Sagar
language : en
Publisher: Springer Nature
Release Date : 2023-07-13
Encyclopedia Of Mathematical Geosciences written by B. S. Daya Sagar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-13 with Science categories.
The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive.
Compositional Data Analysis In Practice
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Author : Michael Greenacre
language : en
Publisher: CRC Press
Release Date : 2018-07-17
Compositional Data Analysis In Practice written by Michael Greenacre and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-17 with Business & Economics categories.
Compositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information. Examples are found in various fields, including geology, medicine, chemistry, agriculture, economics, social science, etc. This concise book presents a very applied introduction to compositional data analysis, focussing on the use of R for analysis. It includes lots of real examples, code snippets, and colour figures, to illustrate the methods.
Visualization And Imputation Of Missing Values
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Author : Matthias Templ
language : en
Publisher: Springer Nature
Release Date : 2023-11-29
Visualization And Imputation Of Missing Values written by Matthias Templ and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-29 with Mathematics categories.
This book explores visualization and imputation techniques for missing values and presents practical applications using the statistical software R. It explains the concepts of common imputation methods with a focus on visualization, description of data problems and practical solutions using R, including modern methods of robust imputation, imputation based on deep learning and imputation for complex data. By describing the advantages, disadvantages and pitfalls of each method, the book presents a clear picture of which imputation methods are applicable given a specific data set at hand. The material covered includes the pre-analysis of data, visualization of missing values in incomplete data, single and multiple imputation, deductive imputation and outlier replacement, model-based methods including methods based on robust estimates, non-linear methods such as tree-based and deep learning methods, imputation of compositional data, imputation quality evaluation from visual diagnostics to precision measures, coverage rates and prediction performance and a description of different model- and design-based simulation designs for the evaluation. The book also features a topic-focused introduction to R and R code is provided in each chapter to explain the practical application of the described methodology. Addressed to researchers, practitioners and students who work with incomplete data, the book offers an introduction to the subject as well as a discussion of recent developments in the field. It is suitable for beginners to the topic and advanced readers alike.