Statistical Theory and Inference

David J. Olive

ISBN 10: 3319049712 / ISBN 13: 9783319049717

Published by Springer International Publishing Mai 2014, 2014

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This text is for a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square error, uniform minimum variance estimators and the Cramer-Rao lower bound, an introduction to large sample theory, likelihood ratio tests and uniformly most powerful tests and the Neyman Pearson Lemma. A major goal of this text is to make these topics much more accessible to students by using the theory of exponential families.Exponential families, indicator functions and the support of the distribution are used throughout the text to simplify the theory. More than 50 ``brand name' distributions are used to illustrate the theory with many examples of exponential families, maximum likelihood estimators and uniformly minimum variance unbiased estimators. There are many homework problems with over 30 pages of solutions. 448 pp. Englisch.

Seller Inventory # 9783319049717

Bibliographic Details

• Title: Statistical Theory and Inference
• Publisher: Springer International Publishing Mai 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 3319049712
• ISBN 13: 9783319049717
• Binding: Buch
• Condition: Neu

About this title

Synopsis

This text is for a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square error, uniform minimum variance estimators and the Cramer-Rao lower bound, an introduction to large sample theory, likelihood ratio tests and uniformly most powerful tests and the Neyman Pearson Lemma. A major goal of this text is to make these topics much more accessible to students by using the theory of exponential families.

Exponential families, indicator functions and the support of the distribution are used throughout the text to simplify the theory. More than 50 ``brand name" distributions are used to illustrate the theory with many examples of exponential families, maximum likelihood estimators and uniformly minimum variance unbiased estimators. There are many homework problems with over 30 pages of solutions.

About the Author

David Olive is an Associate Professor in the Department of Mathematics at Southern Illinois University.