Local Polynomial Modelling and Its Applications describes data-analytic approaches to regression problems encountered in many scientific disciplines. These nonparametric methods are powerful in exploring fine structural relationships and yield useful diagnostic tools for parametric models. The authors emphasize methodologies rather than on theory, present high-dimensional data-analytic tools, and furnish a variety of examples. This is a valuable reference for research and applied statisticians and serves particularly well as a textbook for graduate students and others interested in nonparametric regression.
Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
Inhaltsverzeichnis
Preface, l. Introduction, 2. Overview of existing methods, 3. Framework for local polynomial regression, 4. Automatic determination of model complexity, 5. Applications of local polynomial modelling, 6. Applications in nonlinear time series, 7. Local polynomial regression for multivariate data, References, Author index, Subject index
