A Graduate Course on Statistical Inference (Springer Texts in Statistics)

has checkmarks in pencil on several chapters, the text is not obscured, Shows minor wear to the cover and pages. The binding is fully intact and secure. This is a good reading or studying copy and has been verified that all pages are legible. 100% satisfaction guaranteed! --- A graduate textbook on theoretical statistics by Bing Li and G. Jogesh Babu, developed from lecture notes for two courses at Pennsylvania State University. It begins with a review of measure and probability theory and the notation used throughout. It then covers classical estimation, hypothesis testing for a single parameter and in the presence of nuisance parameters, basic Bayesian ideas, and Bayesian inference, including empirical Bayes and Stein's estimator. Limit theorems and Hilbert-space projections are introduced to support the asymptotic chapters. These include a systematic treatment of regular estimates, covering contiguity, Le Cam's lemmas, convolution theory and asymptotic efficiency, along with asymptotic testing against local alternatives. One chapter uses estimating equations to bring together generalized linear models, generalized estimating equations, quasi-likelihood and conditional inference. A small, shared set of assumptions, such as differentiability under the integral sign and stochastic equicontinuity, is used throughout the book.…