Statistical Theory
A Concise Introduction
Seiten
2022
|
2nd edition
Chapman & Hall/CRC (Verlag)
978-1-032-00745-8 (ISBN)
Chapman & Hall/CRC (Verlag)
978-1-032-00745-8 (ISBN)
Designed for a one-semester advanced undergraduate or graduate statistical theory course, this book clearly explains the underlying ideas, mathematics, and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, linear models etc.
Designed for a one-semester advanced undergraduate or graduate statistical theory course, Statistical Theory: A Concise Introduction, Second Edition clearly explains the underlying ideas, mathematics, and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, linear models, nonparametric statistics, and elements of decision theory. It introduces these topics on a clear intuitive level using illustrative examples in addition to the formal definitions, theorems, and proofs.
Based on the authors’ lecture notes, the book is self-contained, which maintains a proper balance between the clarity and rigor of exposition. In a few cases, the authors present a "sketched" version of a proof, explaining its main ideas rather than giving detailed technical mathematical and probabilistic arguments.
Features:
Second edition has been updated with a new chapter on Nonparametric Estimation; a significant update to the chapter on Statistical Decision Theory; and other updates throughout
No requirement for heavy calculus, and simple questions throughout the text help students check their understanding of the material
Each chapter also includes a set of exercises that range in level of difficulty
Self-contained, and can be used by the students to understand the theory
Chapters and sections marked by asterisks contain more advanced topics and may be omitted
Special chapters on linear models and nonparametric statistics show how the main theoretical concepts can be applied to well-known and frequently used statistical tools
The primary audience for the book is students who want to understand the theoretical basis of mathematical statistics—either advanced undergraduate or graduate students. It will also be an excellent reference for researchers from statistics and other quantitative disciplines.
Designed for a one-semester advanced undergraduate or graduate statistical theory course, Statistical Theory: A Concise Introduction, Second Edition clearly explains the underlying ideas, mathematics, and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, linear models, nonparametric statistics, and elements of decision theory. It introduces these topics on a clear intuitive level using illustrative examples in addition to the formal definitions, theorems, and proofs.
Based on the authors’ lecture notes, the book is self-contained, which maintains a proper balance between the clarity and rigor of exposition. In a few cases, the authors present a "sketched" version of a proof, explaining its main ideas rather than giving detailed technical mathematical and probabilistic arguments.
Features:
Second edition has been updated with a new chapter on Nonparametric Estimation; a significant update to the chapter on Statistical Decision Theory; and other updates throughout
No requirement for heavy calculus, and simple questions throughout the text help students check their understanding of the material
Each chapter also includes a set of exercises that range in level of difficulty
Self-contained, and can be used by the students to understand the theory
Chapters and sections marked by asterisks contain more advanced topics and may be omitted
Special chapters on linear models and nonparametric statistics show how the main theoretical concepts can be applied to well-known and frequently used statistical tools
The primary audience for the book is students who want to understand the theoretical basis of mathematical statistics—either advanced undergraduate or graduate students. It will also be an excellent reference for researchers from statistics and other quantitative disciplines.
Felix Abramovich is a professor at the Department of Statistics and Operations Research at Tel Aviv University. Ya’acov Ritov is a professor in the Department of Statistics at the University of Michigan, Ann Arbor. He is a professor emeritus of Statistics at the Hebrew University of Jerusalem.
1. Introduction. 2. Point Estimation. 3. Confidence Intervals, Bounds, and Regions. 4. Hypothesis testing. 5. Asymptotic Analysis. 6. Bayesian Inference. 7. Elements of Statistical Decision Theory. 8. Linear Models. 9. Nonparametric Estimation.
| Erscheinungsdatum | 12.12.2022 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Texts in Statistical Science |
| Zusatzinfo | 8 Tables, black and white; 27 Line drawings, black and white; 27 Illustrations, black and white |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 600 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| ISBN-10 | 1-032-00745-1 / 1032007451 |
| ISBN-13 | 978-1-032-00745-8 / 9781032007458 |
| Zustand | Neuware |
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