ABSTRACT
Exploratory data analysis (EDA), introduced by Tukey, is a fundamental class of statistical methods for understanding the structure of data sets. Graphical techniques occupy a central place in EDA, as appropriate visual summaries have the ability to convey concisely the deep structure of the data which would otherwise be difficult to discern from purely numerical summaries. Density estimation can be considered as the base case for all other data smoothing problems for EDA. So the success in practical and theoretical terms of kernel methods here has seen kernel estimators being applied to more complex data analysis situations. The development of biotechnologies is one of the major generators of experimental data. The availability of these data, in terms of both quality and quantity, is the driver of the corresponding development of quantitative data analysis in the biological sciences. The chapter also presents an overview of the key concepts discussed in this book.
