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Analysis of Variance for High-Dimensional Data
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Analysis of Variance for High-Dimensional Data : Applications in Life, Food, and Chemical Sciences

Book Details

Format Hardback or Cased Book
ISBN-10 139421121X
ISBN-13 9781394211210
Publisher John Wiley & Sons Inc
Imprint John Wiley & Sons Inc
Country of Manufacture GB
Country of Publication GB
Publication Date Sep 11th, 2025
Print length 336 Pages
Product Classification: Chemistry
Ksh 21,600.00
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Overview of methods for analyzing high-dimensional experimental data, including theory, methodologies, and applications Analysis of Variance for High-Dimensional Data summarizes all the methods to analyze high-dimensional data that are obtained through applying an experimental design in the life, food, and chemical sciences, especially those developed in recent years. Written by international experts who lead development in the field, Analysis of Variance for High-Dimensional Data includes information on: Basic and established theories on linear models from a mathematical and statistical perspectiveAvailable methods and their mutual relationships, including coverage of ASCA, APCA, PC-ANOVA, ASCA+, LiMM-PCA and RM-ASCA+, and PERMANOVA, as well as various alternative methods and extensionsApplications in metabolomics, microbiome, gene expression, proteomics, food science, sensory science, and chemistryCommercially available and open-source software for application of these methods Analysis of Variance for High-Dimensional Data is an essential reference for practitioners involved in data analysis in the natural sciences, including professionals working in chemometrics, bioinformatics, data science, statistics, and machine learning. The book is valuable for developers of new methods in high dimensional data analysis.
Overview of methods for analyzing high-dimensional experimental data, including theory, methodologies, and applications Analysis of Variance for High-Dimensional Data summarizes all the methods to analyze high-dimensional data that are obtained through applying an experimental design in the life, food and chemical sciences, especially those developed in recent years. Written by international experts who lead development in the field, Analysis of Variance for High-Dimensional Data includes information on: Basic and established theories on linear models from a mathematical and statistical perspectiveAvailable methods and their mutual relationships, including coverage of ASCA, APCA, PC-ANOVA, ASCA+, LiMM-PCA and RM-ASCA+, and PERMANOVA, as well as various alternative methods and extensionsApplications in metabolomics, microbiome, gene expression, proteomics, food science, sensory science, and chemistryCommercially available and open-source software for application of these methods Analysis of Variance for High-Dimensional Data is an essential reference for practitioners involved in data analysis in the natural sciences, including professionals working in chemometrics, bioinformatics, data science, statistics, and machine learning. The book is valuable for developers of new methods in high dimensional data analysis.

Overview of methods for analyzing high-dimensional experimental data, including theory, methodologies, and applications

Analysis of Variance for High-Dimensional Data summarizes all the methods to analyze high-dimensional data that are obtained through applying an experimental design in the life, food and chemical sciences, especially those developed in recent years.

Written by international experts who lead development in the field, Analysis of Variance for High-Dimensional Data includes information on:

  • Basic and established theories on linear models from a mathematical and statistical perspective
  • Available methods and their mutual relationships, including coverage of ASCA, APCA, PC-ANOVA, ASCA+, LiMM-PCA and RM-ASCA+, and PERMANOVA, as well as various alternative methods and extensions
  • Applications in metabolomics, microbiome, gene expression, proteomics, food science, sensory science, and chemistry
  • Commercially available and open-source software for application of these methods

Analysis of Variance for High-Dimensional Data is an essential reference for practitioners involved in data analysis in the natural sciences, including professionals working in chemometrics, bioinformatics, data science, statistics, and machine learning. The book is valuable for developers of new methods in high dimensional data analysis.


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