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Large sample techniques for statistics / Jiming Jiang.

Jiang, Jiming, author.
2022
QA276.6 .J53 2022
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Title
Large sample techniques for statistics / Jiming Jiang.
Author
Jiang, Jiming, author.
Edition
Second edition.
ISBN
9783030916954 (electronic bk.)
3030916952 (electronic bk.)
9783030916947 (print)
3030916944 (print)
DOI
https://doi.org/10.1007/978-3-030-91695-4
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (some color).
Item Number
10.1007/978-3-030-91695-4 doi
Call Number
QA276.6 .J53 2022
Dewey Decimal Classification
519.5/2
Summary
This book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, Large Sample Techniques for Statistics begins with fundamental techniques, and connects theory and applications in engaging ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. The book's case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science. This text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed April 12, 2022).
Series
Springer texts in statistics.
Available in Other Form
Print version: 9783030916947
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Chapter 1. The [epsilon]-[delta] arguments
Chapter 2. Modes of Convergence
Chapter 3. Big O, Small o, and the Unspecified c
Chapter 4. Asymptotic Expansions
Chapter 5. Inequalities
Chapter 6. Sums of Independent Random Variables
Chapter 7. Empirical Processes
Chapter 8. Martingales
Chapter 9. Time and Spatial Series
Chapter 10. Stochastic Processes
Chapter 11. Nonparametric Statistics
Chapter 12. Mixed Effects Models
Chapter 13. Small-Area Estimation
Chapter 14. Jackknife and Bootstrap
Chapter 15. Markov-Chain Monte Carlo
Chapter 16. Random Matrix Theory.

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