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Large sample techniques for statistics

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Large sample techniques for statistics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Large sample techniques for statistics/ by Jiming Jiang.
Author:	Jiang, Jiming.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xv, 685 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Sampling (Statistics) -
Online resource:	https://doi.org/10.1007/978-3-030-91695-4
ISBN:	9783030916954

Large sample techniques for statistics
Jiang, Jiming.

Large sample techniques for statistics[electronic resource] /by Jiming Jiang. - Second edition. - Cham :Springer International Publishing :2022. - xv, 685 p. :ill. (some col.), digital ;24 cm. - Springer texts in statistics,2197-4136. - Springer texts in statistics..

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.

ISBN: 9783030916954

Standard No.: 10.1007/978-3-030-91695-4doiSubjects--Topical Terms:

545623
Sampling (Statistics)

LC Class. No.: QA276.6 / .J53 2022

Dewey Class. No.: 519.52

Large sample techniques for statistics
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300 $a xv, 685 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 2197-4136
520 $a 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.
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