Large Sample Techniques For Statistics (springe... Today

This post provides an overview of , authored by Jiming Jiang and published as part of the Springer Texts in Statistics series. Book Overview

Reviews basic tools like epsilon-delta arguments, Taylor expansion, types of convergence, and inequalities. Large Sample Techniques for Statistics (Springe...

A foundational course in calculus and mathematical statistics is required. Why It Matters This post provides an overview of , authored

Detailed reviews and purchasing options are available on platforms like Amazon and Parnassus Books . Why It Matters Detailed reviews and purchasing options

It is designed for a broad academic range, from senior undergraduates to doctoral researchers.

The book is divided into 16 chapters, with the first ten including case studies to demonstrate real-world utility. Key Topics:

Large-sample techniques are essential because they provide solutions for complex problems where exact distributions are intractable. As noted by Jiming Jiang in the preface, these techniques simplify and justify statistical solutions while guiding researchers toward better methods, though he warns that misuse can lead to serious errors, such as misinterpreting the asymptotic null distribution of a likelihood ratio test. Hardcover: ISBN 978-3-030-91694-7 Paperback: ISBN 978-3-030-91697-8 eBook: ISBN 978-3-030-91695-4