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Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists addresses core issues in post-calculus probability and statistics in a way that is useful for statistics and mathematics majors as well as students in the quantitative sciences. The book's conversational tone, which provides the mathematical justification behind widely used statistical methods in a reader-friendly manner, and the book's many examples, tutorials, exercises and problems for solution, together constitute an effective resource that students can read and learn from and instructors can count on as a worthy complement to their lectures.
Provides a Solid Foundation for Statistical Modeling and Inference and Demonstrates its Breadth of Applicability
Carefully introduces the probability models and tools that are essential for studying statistical theory and applications
Treats statistical estimation from a host of different perspectives, with a full chapter on the Bayesian approach and a section on nonparametric estimation
Details both the theory and practice of hypothesis testing, accompanied by careful advice about potential misuses
Shows how estimation and testing theory may be applied to data obeying regression or ANOVA models
Includes sections on Newton-Raphson iterations, on the EM algorithm, on odds ratio estimation in cohort studies and on the nonparametric bootstrap
Using classroom-tested approaches that engage students in active learning, the text offers instructors the flexibility to control the mathematical level of their course. It contains the mathematical detail that is expected in a course for ´majors´ but is written in a way that emphasizes the intuitive content in statistical theory and the way theoretical results are used in practice. More than 1000 exercises and problems at varying levels of difficulty and with a broad range of topical focus give instructors many options in assigning homework and provide students with many problems on which to practice and from which to learn.