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“Resampling” as a book category encompasses works focused on computational methods that reuse observed data (or signals) to estimate uncertainty, test hypotheses, validate and select models, or transform data without heavy parametric assumptions. Core topics include the bootstrap, jackknife, permutation/randomization tests, Monte Carlo methods, cross-validation, and extensions like block bootstrap for dependent data, subsampling/bagging and class-imbalance techniques in machine learning, with practical implementations in R/Python/MATLAB. The category may also include signal-processing resampling (interpolation/decimation and sample‑rate conversion). These books emphasize hands-on, assumption-lean inference and model assessment across statistics, data science, and engineering.

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