Preface

Summary

This book is about data in many – and sometimes very many – variables and about analysing such data. The book attempts to integrate classical multivariate methods with contemporary methods suitable for high-dimensional data and to present them in a coherent and transparent framework. Writing about ideas that emerged more than a hundred years ago and that have become increasingly relevant again in the last few decades is exciting and challenging. With hindsight, we can reflect on the achievements of those who paved the way, whose methods we apply to ever bigger and more complex data and who will continue to influence our ideas and guide our research. Renewed interest in the classical methods and their extension has led to analyses that give new insight into data and apply to bigger and more complex problems.

There are two players in this book: Theory and Data. Theory advertises its wares to lure Data into revealing its secrets, but Data has its own ideas. Theory wants to provide elegant solutions which answer many but not all of Data's demands, but these lead Data to pose new challenges to Theory. Statistics thrives on interactions between theory and data, and we develop better theory when we ‘listen’ to data. Statisticians often work with experts in other fields and analyse data from many different areas. We, the statisticians, need and benefit from the expertise of our colleagues in the analysis of their data and interpretation of the results of our analysis.