This book is on data-analytic approaches to regression problems arising from many scientific disciplines. These approaches are also called nonparametric regression in the literature. The aim of non parametric methods is to relax assumptions on the form of a regres sion function, and to let data search for a suitable function that describes well the available data. These approaches are powerful in exploring fine structural relationships and provide very useful diagnostic tools for parametric models. Over the last two decades, vast efforts have been devoted to nonparametric regression analyses. This book hopes to bring an up-to-date picture on the state of the art of nonparametric regres sion techniques. The emphasis of this book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. These problems include least squares regression, quantile and robust re gression, survival analysis, generalized linear models and nonlinear time series. Local polynomial modelling is employed in a large frac tion of the book, but other key ideas of nonparametric regression are also discussed.