Get personalized book recommendations based on your reading history and preferences. Our algorithm analyzes your favorite books and reading patterns to suggest your next great read.

Nonparametric statistics books focus on methods that make minimal assumptions about the form of underlying populations, emphasizing distribution-free and rank-based inference, resampling techniques, and flexible function estimation. Typical topics include nonparametric hypothesis tests (e.g., sign, Wilcoxon, Kruskal–Wallis, Kolmogorov–Smirnov), permutation tests and the bootstrap, order statistics and empirical processes, density and distribution estimation (kernel density estimation, histograms), and nonparametric regression and smoothing (kernels, splines, local polynomials, GAMs), along with survival methods like the Kaplan–Meier estimator; some volumes also connect to nonparametric machine learning (k-nearest neighbors, decision trees, random forests) and semiparametric models (e.g., Cox proportional hazards). The category spans rigorous theory (consistency, asymptotics, convergence rates) and applied guides with software implementations and case studies across biostatistics, economics, psychology, and engineering. Aimed at statisticians, data scientists, and researchers, these books highlight robust, assumption-light inference and prediction for settings with small samples, outliers, ordinal data, or when parametric models are unreliable.

Filter books by their publication year. Enter the earliest year (Start) and latest year (End) to find books published within that period. Leave either field empty to search from the beginning of time or up to the present day.