Statistical Inference 2nd Edition

📚📚 I. THÔNG TIN SẢN PHẨM
📒 Mã sản phẩm :  STT1470
📒 Nhà xuất bản : Chapman and Hall/CRC; 2nd edition (May 23, 2024)
📒 Tác giả : George Casella, Roger Berger 
📒 ISBN      : 1032593032
📒 Số trang : 566 trang
📒 Hình thức : Bìa Mềm, in ĐEN TRẮNG 
📒 Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
📒 Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
📒 Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

📚📚 II. MÔ TẢ SẢN PHẨM
📒 1.Mô tả sản phẩm
This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation.

Features

• The classic graduate-level textbook on statistical inference
• Develops elements of statistical theory from first principles of probability
• Written in a lucid style accessible to anyone with some background in calculus
• Covers all key topics of a standard course in inference
• Hundreds of examples throughout to aid understanding
• Each chapter includes an extensive set of graduated exercises
• Statistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background. It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations.

📒 2. Tác giả
Professor George Casella completed his undergraduate education at Fordham University and graduate education at Purdue University. He served on the faculty of Rutgers University, Cornell University, and the University of Florida. His contributions focused on the area of statistics including Monte Carlo methods, model selection, and genomic analysis. He was particularly active in Bayesian and empirical Bayes methods, with works connecting with the Stein phenomenon, on assessing and accelerating the convergence of Markov chain Monte Carlo methods, as in his Rao-Blackwellisation technique, and recasting lasso as Bayesian posterior mode estimation with independent Laplace priors.