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

Über die Autorin bzw. den Autor

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.

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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 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.