Introduction to Mathematical Statistics (8th Edition) by Robert V. Hogg, Joseph W. McKean & Allen T. Craig — A comprehensive, rigorous yet accessible introduction to mathematical statistics, offering in‑depth treatment of probability distributions, infere

Introduction to Mathematical Statistics (8th Edition) by Hogg, McKean & Craig is a cornerstone textbook for students who want to transition from basic probability or applied statistics into the rigorous realm of statistical theory. This edition offers a solid foundation in both probability models and statistical inference, bridging the gap between theory and application. The text is structured around major statistical topics: it begins with the definition of probability spaces, random variables, distributions (both univariate and multivariate), and introduces key distributions relevant for inference—such as the normal, chi‑square, t, and F‑distributions. Adlibris It then moves into estimation theory (consistency, efficiency, bias), explores maximum likelihood estimation, and presents the concept of sufficiency and the factorization theorem. Hypothesis testing is treated comprehensively including optimal tests, Neyman‑Pearson lemma, and generalised likelihood ratio tests. Further chapters delve into inferences for the normal linear model, touching on regression, analysis of variance, and the connection between statistical theory and practice. A significant feature of this edition is the inclusion of nonparametric and robust statistics, and Bayesian methods—highlighting modern trends in statistical inference. According to Pearson’s description, this 8th edition has been updated with many additional real‑data sets, expanded R software usage, and improved linkage between theory and practice. Pearson Europe +2 Pearson +2 The authors place great emphasis on examples, illustrative problems and exercises that foster deeper understanding. The flexible organisation allows instructors to tailor the course—selecting core chapters or extending into optional topics such as Bayesian inference. The inclusion of the R statistical software makes the book relevant for today’s data‑driven environments; students learn not only theory but how to apply it with computing tools. WorldCat For students, the text demands engagement with proofs, derivations and rigorous reasoning—pushing beyond algorithmic application toward a true conceptual grasp of statistical inference. For instructors, it provides a rich set of exercises, varying in difficulty, that can be used to teach theory or application. For practitioners or self‑learners aiming to deepen their statistical grounding, it remains a comprehensive reference.