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"Statistical estimation" as a book category covers methods and theory for inferring unknown quantities from data. It includes core topics such as point and interval estimation, properties of estimators (bias, consistency, efficiency), classical techniques like maximum likelihood and method of moments, Bayesian estimation and credible intervals, shrinkage and penalized estimators, and resampling and computational approaches (bootstrap, MCMC). Books in this category range from applied, software-oriented guides that focus on practical implementation and real-world examples to rigorous texts treating asymptotic theory, decision-theoretic frameworks, and optimality results; they commonly require background in probability and basic calculus and often address applications across sciences, economics, engineering, and machine learning.

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