What is a Statistician?

DeGroot, Morris H. DeGroot (19311989) World-renowned statistician. He was a member of the International Statistical Association, the American Statistical Association, the Mathematical Statistics Society, and the Econometric Society. Professor of Carnegie Mellon University, founded the school's statistics department in 1966. DeGroot is very active academically and has many achievements. He is the author of Probability and Statistics, Optimal Statistical Decisions, and Statistics and the Law. To commemorate the contribution of his work to the teaching of statistics, the International Bayesian Analytical Society has specially established the DeGroot Award to recognize outstanding statistical works.

Degrut

(Statistician)

Right!

DeGroot, Morris H. DeGroot (19311989) World-renowned statistician. He was a member of the International Statistical Association, the American Statistical Association, the Mathematical Statistics Society, and the Econometric Society. Professor of Carnegie Mellon University, founded the school's statistics department in 1966. DeGroot is very active academically and has many achievements. He is the author of Probability and Statistics, Optimal Statistical Decisions, and Statistics and the Law. To commemorate the contribution of his work to the teaching of statistics, the International Bayesian Analytical Society has specially established the DeGroot Award to recognize outstanding statistical works.

Chinese name

Degrut

Country of Citizenship

Morris H. DeGroot (19311989) was born

"Probability and Statistics" evaluates this is a model textbook of probability theory and mathematical statistics, adopted by many well-known universities, including Carnegie Mellon University, Harvard University, MIT, Washington University, Duke University, University of California, Los Angeles, etc.

Probability theory has always been known as abstract and difficult to learn, and the author of this book explains the theory and proofs with a large number of examples to make the content easy to understand. The examples in this book cover a wide range. In addition to explaining some basic concepts of probability through some well-known examples, some new examples have been added to describe the application of probability theory in genetics, queue theory, and computational finance. It also integrates some of the current research frontiers into the textbook.

This book is rich and complete. In addition to classic probability theory, there is also a large part of the introduction to estimation methods (maximum likelihood estimation, Bayesian estimation, least squares), and discusses statistical testing and other non-parametric methods and random Simulation, etc.

brief introduction

The content of this book includes two parts: introduction and mathematical statistics, including conditional probability, random variables and their distributions, mathematical expectations, special distributions, estimates, sampling distributions of estimators, hypothesis testing, categorical data and non-parameters and methods, linear statistical models, Stochastic simulation, etc. The knowledge architecture of this book is basically consistent with the domestic mainstream probability theory and mathematical statistics textbooks, but the content selection and the arrangement of example questions are relatively new, especially the addition of some very practical and advanced simulation methods. The book finally provides Solutions and indexes for odd-numbered exercises.

This book is used as a teaching material for probability theory and mathematical statistics, suitable for senior undergraduates and graduate students as a textbook or reference book, and also can be used as a reference book by statistical staff.