Mark J. Schervish Livres

The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Probability & Statistics, Fourth Edition, was written for a one- or two-semester probability and statistics course. This course is offered primarily at four-year institutions and taken mostly by sophomore and junior level students majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.

This graduate textbook offers an advanced course in statistics, focusing on estimation, testing, and large sample theory essential for Ph.D. preparation. It presents a mathematically rigorous exploration of both Classical and Bayesian inference, contrasting approaches like "uniformly most powerful" testing with decision-theoretic methods.