Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66 (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

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.

• Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics)
• Introduction to Nonparametric Estimation (Springer Series in Statistics)
• Empirical Likelihood
• A Practical Guide to Splines (Applied Mathematical Sciences)
• Bootstrap Methods and their Application (Cambridge Series in Statistical and Probabilistic Mathematics)
• Mathematical Foundations of Infinite-Dimensional Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics)
• Quantile Regression (Econometric Society Monographs)
• Advanced Econometrics
• Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition
• Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)