Theory of Statistics

This document originated from lecture notes of mathematical statistics course taught at George Mason University. Students should be able to work through the details of "hard" proofs and derivations.

**Tag(s):**
Statistics

**Publication date**: 12 Nov 2013

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
917 pages

**Views**: 13,727

Theory of Statistics

This document originated from lecture notes of mathematical statistics course taught at George Mason University. Students should be able to work through the details of "hard" proofs and derivations.

From the Preface:

James E. Gentle wrote:This document is directed toward students for whom the theory of statistics is or will become an important part of their lives. Obviously, such students should be able to work through the details of "hard" proofs and derivations; that is, students should master the fundamentals of mathematical statistics. In addition, students at this level should acquire, or begin acquiring, a deep appreciation for the field, including its historical development and its relation to other areas of mathematics and science generally; that is, students should master the fundamentals of the broader theory of statistics. Some of the chapter endnotes are intended to help students gain such an appreciation by leading them to background sources and also by making more subjective statements than might be made in the main body.

Tweet

About The Author(s)

James E. Gentle is Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. His interests include statistical computing, computational statistics, simulation, robust statistics, survey sampling, and computational finance.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Microsoft .NET
Rexx
Perl
PHP
Python
R
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors