Self-Normalized Processes

Victor H. de la Pena is Fellow of Institute of Mathematical Statistics and a Medallion Lecturer for IMS in 2007. Tze Leung LAI: Distinguished Lecture Series in Statistical Science from Academia Sinica (2001), Starr Lectures in Financial Mathematics from the University of Hong Kong (2001), Center for Advanced Study in the Behavioral Sciences Fellowship (1999-2000), Richard Anderson Lecture in Statistics from University of Kentucky (1999), Election to Academia Sinica (1994), Committee of Presidents of Statistical Societies Award (1983), John Simon Guggenheim Fellowship (1983-84). Qi-Man SHAO is Associate Editor of 5 top journals and co-author of: Chen, M. H., Shao, Q. M. and Ibrahim, J.G. (2000) , Monte Carlo Methods In Bayesian Computation . Springer Series in Statistics, Springer-Verlag , New York. ISBN 0-387-98935-8

From the reviews: "Readership: Research workers in applied probability. ... it serves as a reference text for a special-topic course for PhD students; each chapter after the first ends with a collection of problems and the material is based on such a course taught by two of the authors at Stanford and Hong kong. ... It is a thorough ... study of an area of applied probability that underlies important statistical methodology. ... I am sure that the text will encourage others to join them in their work." (Martin Crowder, International Statistical Review, Vol. 77 (3), 2009) "The monograph will certainly be of great use as a reference text for researchers working on corresponding problems, but also for Ph.D. and other advanced students who want to learn about the techniques and relevant topics in an interesting and active research area. ... this monograph provides a very useful collection of recent and earlier research results in the theory and applications of self-normalized processes and can be used as a standard reference text by graduate students and researchers in the field." (Josef Steinebach, Zentralblatt MATH, Vol. 1165, 2009) "This book covers recent developments on self-normalized processes, emphasizing important advances in the area. It is the first book that systematically treats the theory and applications of self-normalized processes. ... In all aspects, this is an excellent book, and it is ideal for a second-year Ph.D. level topics course. It is also a great book for anyone who is interested in research in self-normalized processes and related areas." (Fuchang Gao, Mathematical Reviews, Issue 2010 d)

• Independent Random Variables.- Classical Limit Theorems, Inequalities and Other Tools.- Self-Normalized Large Deviations.- Weak Convergence of Self-Normalized Sums.- Stein's Method and Self-Normalized Berry–Esseen Inequality.- Self-Normalized Moderate Deviations and Laws of the Iterated Logarithm.- Cramér-Type Moderate Deviations for Self-Normalized Sums.- Self-Normalized Empirical Processes and U-Statistics.- Martingales and Dependent Random Vectors.- Martingale Inequalities and Related Tools.- A General Framework for Self-Normalization.- Pseudo-Maximization via Method of Mixtures.- Moment and Exponential Inequalities for Self-Normalized Processes.- Laws of the Iterated Logarithm for Self-Normalized Processes.- Multivariate Self-Normalized Processes with Matrix Normalization.- Statistical Applications.- The t-Statistic and Studentized Statistics.- Self-Normalization for Approximate Pivots in Bootstrapping.- Pseudo-Maximization in Likelihood and Bayesian Inference.- Sequential Analysis and Boundary Crossing Probabilities for Self-Normalized Statistics.