Nonparametric Inference (Chapman & Hall/CRC Texts in Statistical Science)

This book provides a comprehensive and balanced treatment of both classical and modern methods in nonparametric inference.It begins with foundational topics such as order statistics, ranks, and confidence intervals for medians and percentiles before progressing to distribution-free tests, robust estimators, regression quantiles and U-statistics.Advanced topics include nonparametric density and regression estimation, model diagnostics, empirical likelihood, and survival analysis, including nonparametric Bayesian and maximum likelihood estimators.The book uniquely integrates these topics into a single resource, making it distinct from other texts in the field. Key Features:A balanced blend of classical methods (e.g., rank and sign tests) and modern techniques (e.g., bootstrap, empirical likelihood, and nonparametric regression). Comprehensive coverage of nonparametric density and regression estimation, model diagnostics, and survival analysis, including Bayesian and maximum likelihood ap