This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain.

This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective.

This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring.

This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers. Unfolding is a related method that maps preference data (typically evaluative ratings of different persons on a set of objects) as distances between two sets of points (representing the persons and the objects, resp.).

Lastly, the book addresses the applications of asymmetric kernel estimation and testing to various forms of nonnegative economic and financial data. Until recently, the most popularly chosen nonparametric methods used symmetric kernel functions to estimate probability density functions of symmetric distributions with unbounded support.