Real data often contain anomalous cases, also known as outliers. These may spoil the
resulting analysis but they may also contain valuable information. In either case, the ability to …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …

When analyzing data, outlying observations cause problems because they may strongly
influence the result. Robust statistics aims at detecting the outliers by searching for the …

Figure 1.1 provides a prototype for the type of data that we shall consider. It shows the
heights of 10 girls measured at a set of 31 ages in the Berkeley Growth Study (Tuddenham …

The viewpoint taken in much of this text is that PCA is mainly a descriptive tool with no need
for rigorous distributional or model assumptions. This implies that it can be used on a wide …

This revised book provides a thorough explanation of the foundation of robust methods,
incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and …

This work is the fruit of recent advances concerning both nonparametric statistical modelling
and functional variables and is based on various publications in international statistical …

The theory and practice of statistical methods in situations where the available data are
functions (instead of real numbers or vectors) is often referred to as Functional Data Analysis …