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..
General
Imprint: |
Springer Nature Switzerland AG
|
Country of origin: |
Switzerland |
Series: |
Springer Texts in Statistics |
Release date: |
April 2023 |
First published: |
2022 |
Authors: |
Jiming Jiang
|
Dimensions: |
235 x 155mm (L x W) |
Pages: |
685 |
Edition: |
2nd ed. 2022 |
ISBN-13: |
978-3-03-091697-8 |
Categories: |
Books
|
LSN: |
3-03-091697-9 |
Barcode: |
9783030916978 |
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