This classic textbook provides a rigorous and systematic treatment of Euclidean geometry intended for readers who have a background in basic mathematics but seek a deeper, proof-oriented understanding. Unlike introductory texts that rely on visual intuition, Moise constructs geometry from a modern axiomatic foundation—specifically focusing on betweenness, congruence, and continuity. The "adva…
This volume is part of a two-volume English translation of the Russian work Geometric Transformations, focusing specifically on collinearity-preserving transformations within the projective plane. It introduces the construction of the projective plane and details essential concepts such as projective mappings, fundamental theorems, cross ratios, and harmonic sets. The text also covers inversion…
Part of the Undergraduate Texts in Mathematics series, this book offers a rigorous introduction to geometry through the study of isometries and transformations. Rather than focusing on classical synthetic proofs, Martin uses group theory to explore the symmetries of the plane, including reflections, rotations, translations, and glide-reflections. It covers sophisticated topics such as the class…
Theory of Rank Tests by Jaroslav Hájek is a fundamental work in nonparametric statistics that develops the mathematical theory of rank-based hypothesis testing. The book focuses on distribution-free methods, providing rigorous treatment of rank tests such as Wilcoxon and related procedures. It explores asymptotic properties, efficiency, and optimality of rank tests, making it a key reference f…
This book provides a systematic study of translation planes, a class of projective planes that admit a large group of elations. It explores their connections to algebraic structures such as quasifields and spreads, and presents key classification results. The work is aimed at researchers and graduate students in combinatorics and finite geometry.
Monte Carlo Methods by Hammersley and Handscomb is a classic foundational text in simulation and computational probability. It develops the theory and practice of Monte Carlo techniques for solving mathematical and statistical problems using random sampling. The book covers random number generation, variance reduction techniques, and applications to integration and stochastic systems. It remain…
Time Series Analysis by E. J. Hannan is a mathematically rigorous treatment of time series theory. The book focuses on the statistical foundations of stationary processes, spectral analysis, and asymptotic theory. It emphasizes theoretical development over applied forecasting, providing deep insight into stochastic processes and estimation methods used in time-dependent data analysis.
Bayes Theory by J. A. Hartigan provides a rigorous mathematical foundation for Bayesian statistical inference. The book develops probability theory from a Bayesian perspective and explores conditional probability, decision theory, and asymptotic behavior of posterior distributions. It also addresses advanced topics such as improper priors, convergence, and nonparametric Bayesian methods. This w…