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..
Chapter
1. The -d 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.
Jiming Jiang is Professor of Statistics and a former Director of Statistical Laboratory at the University of California, Davis. He is a prominent researcher in the fields of mixed effects models, small area estimation, model selection, and statistical genetics. He is the author of Linear and Generalized Linear Mixed Models and Their Applications, 2nd Edition (Springer 2021), Robust Mixed Model Analysis (2019), Asymptotic Analysis of Mixed Effects Models: Theory, Applications, and Open Problems (2017), and The Fence Methods (with T. Ngyuen, 2016). Jiming Jiang has been editorial board member of The Annals of Statistics and Journal of the American Statistical Association, among others. He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics; an elected member of the International Statistical Institute; and a Yangtze River Scholar (Chaired Professor, 2017-2020).
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