A Course in the Large Sample Theory of Statistical Inference 1st Edition

A Course in the Large Sample Theory of Statistical Inference (1st Edition) by W. Jackson Hall and David Oakes is a rigorous, accessible guide to the asymptotic tools that underpin modern statistical practice. This authoritative volume draws readers into the logic of large-sample methods—consistency, asymptotic normality, efficiency, likelihood theory, estimating equations, the delta method, contiguity and local asymptotic normality—presented with clarity and mathematical precision.

Carefully structured for graduate students, applied statisticians, econometricians and biostatisticians, the book balances formal proofs with intuitive explanations and real-world motivation. Each chapter builds practical competence: you’ll learn how to derive asymptotic distributions for estimators and test statistics, assess estimator performance, and apply these principles across regression models, generalized estimating equations, and likelihood-based inference. The text’s emphasis on conceptual understanding makes it valuable for researchers designing studies or analyzing large datasets in academia and industry.

Ideal for use in advanced courses and as a reference for practitioners, this 1st Edition is especially relevant to readers in North America, Europe and Asia seeking a dependable foundation in asymptotic theory. Whether preparing for doctoral research, strengthening theoretical foundations for applied work, or integrating large-sample techniques into machine learning and econometric workflows, this book equips you with the tools to reason rigorously about inference in large samples.

Add A Course in the Large Sample Theory of Statistical Inference to your professional library and master the asymptotic methods that power modern statistical analysis.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.