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Catalogue Information
Field name	Details
Dewey Class	519.2
Title	Large Sample Techniques for Statistics ( EBook/) / by Jiming Jiang.
Author	Jiang, Jiming
Other name(s)	SpringerLink (Online service)
Edition statement	2nd ed. 2022.
Publication	Cham : : Springer International Publishing : : Imprint: Springer, , 2022.
Physical Details	XV, 685 p. 9 illus., 2 illus. in color. : online resource.
Series	Springer Texts in Statistics 2197-4136
ISBN	9783030916954
Summary Note	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.:
Contents note	Chapter 1. The -δ 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.
Mode of acces to digital resource	Digital reproduction.-
	Cham :
	Springer International Publishing,
	2022. -
	Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site	https://doi.org/10.1007/978-3-030-91695-4
Links to Related Works	Subject References: Probabilities . Probability Theory . Statistical Theory and Methods . Statistics . Authors: author . Jiang, Jiming . Corporate Authors: SpringerLink (Online service) . Series: Springer Texts in Statistics . Classification: 519.2 .

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Large Sample Techniques for Statistics

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