Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Mathematical statistics --- Distribution (Probability theory) --- Random variables --- Distribution (Théorie des probabilités) --- Statistique mathématique --- Variables aléatoires --- 519.216 --- Chance variables --- Stochastic variables --- Probabilities --- Variables (Mathematics) --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Stochastic processes in general. Prediction theory. Stopping times. Martingales --- Statistical methods --- 519.216 Stochastic processes in general. Prediction theory. Stopping times. Martingales --- Distribution (Théorie des probabilités) --- Statistique mathématique --- Variables aléatoires

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book provides an account of weak convergence theory, empirical processes, and their application to a wide variety of problems in statistics. The first part of the book presents a thorough treatment of stochastic convergence in its various forms. Part 2 brings together the theory of empirical processes in a form accessible to statisticians and probabilists. In Part 3, the authors cover a range of applications in statistics including rates of convergence of estimators; limit theorems for M− and Z−estimators; the bootstrap; the functional delta-method and semiparametric estimation. Most of the chapters conclude with “problems and complements.” Some of these are exercises to help the reader’s understanding of the material, whereas others are intended to supplement the text. This second edition includes many of the new developments in the field since publication of the first edition in 1996: Glivenko-Cantelli preservation theorems; new bounds on expectations of suprema of empirical processes; new bounds on covering numbers for various function classes; generic chaining; definitive versions of concentration bounds; and new applications in statistics including penalized M-estimation, the lasso, classification, and support vector machines. The approximately 200 additional pages also round out classical subjects, including chapters on weak convergence in Skorokhod space, on stable convergence, and on processes based on pseudo-observations.

Statistics. --- Applied Statistics. --- Statistical Theory and Methods. --- Bayesian Inference. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Processos estocàstics --- Convergència (Matemàtica) --- Distribució (Teoria de la probabilitat) --- Mostreig (Estadística)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This is a collection of papers by participants at the High Dimensional Probability VI Meeting held from October 9-14, 2011 at the Banff International Research Station in Banff, Alberta, Canada.  High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other areas of mathematics, statistics, and computer science. These include random matrix theory, nonparametric statistics, empirical process theory, statistical learning theory, concentration of measure phenomena, strong and weak approximations, distribution function estimation in high dimensions, combinatorial optimization, and random graph theory. The papers in this volume show that HDP theory continues to develop new tools, methods, techniques and perspectives to analyze the random phenomena. Both researchers and advanced students will find this book of great use for learning about new avenues of research.

Numerical methods of optimisation --- Operational research. Game theory --- Probability theory --- Mathematics --- waarschijnlijkheidstheorie --- stochastische analyse --- wiskunde --- kansrekening --- optimalisatie