Analysis of Variance for High-Dimensional Data

Age K. Smilde, Federico Marini, Johan A. Westerhuis, Kristian Hovde Liland

ISBN 10: 139421121X / ISBN 13: 9781394211210

Published by John Wiley and Sons Inc, US, 2025

About this Item

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.

Seller Inventory # LU-9781394211210

Bibliographic Details

• Title: Analysis of Variance for High-Dimensional Data
• Publisher: John Wiley and Sons Inc, US
• Publication Date: 2025
• Language: English
• ISBN 10: 139421121X
• ISBN 13: 9781394211210
• Binding: Hardback
• Condition: New
• Weight: 907 grams

About this title

Synopsis

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.

About the Author

Age K. Smilde is Emeritus-Professor of Biosystems Data Analysis at the Swammerdam Institute for Life Sciences at the University of Amsterdam. He also holds a part-time position at the Department of Plant and Environmental Sciences at the University of Copenhagen.

Federico Marini is Professor of Analytical Chemistry at the Department of Chemistry of the University of Rome “La Sapienza”.

Johan A. Westerhuis is Assistant Professor at the Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands.

Kristian H. Liland is Professor of Statistics at the Faculty of Science and Technology, Norwegian University of Life Sciences, Norway.