Bøker av Age K. (University of Amsterdam Smilde

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.

Multiblock Data Fusion in Statistics and Machine LearningExplore the advantages and shortcomings of various forms of multiblock analysis, and the relationships between them, with this expert guideArising out of fusion problems that exist in a variety of fields in the natural and life sciences, the methods available to fuse multiple data sets have expanded dramatically in recent years. Older methods, rooted in psychometrics and chemometrics, also exist.Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is a detailed overview of all relevant multiblock data analysis methods for fusing multiple data sets. It focuses on methods based on components and latent variables, including both well-known and lesser-known methods with potential applications in different types of problems.Many of the included methods are illustrated by practical examples and are accompanied by a freely available R-package. The distinguished authors have created an accessible and useful guide to help readers fuse data, develop new data fusion models, discover how the involved algorithms and models work, and understand the advantages and shortcomings of various approaches.This book includes:* A thorough introduction to the different options available for the fusion of multiple data sets, including methods originating in psychometrics and chemometrics* Practical discussions of well-known and lesser-known methods with applications in a wide variety of data problems* Included, functional R-code for the application of many of the discussed methodsPerfect for graduate students studying data analysis in the context of the natural and life sciences, including bioinformatics, sensometrics, and chemometrics, Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is also an indispensable resource for developers and users of the results of multiblock methods.

Multi--way analysis is an increasingly important method of data analysis which can be used in a large number of fields in chemistry and other disciplines. This book is an introduction to the field of multi--way analysis for chemists and chemometricians, focusing on the ideas behind the method and its practical applications.