This volume is the first in the new series Nonlinear Time Series and Chaos. The general aim of the series is to provide a bridge between the two communities by inviting prominent researchers in their respective fields to give a systematic account of their chosen topics, starting at the beginning and ending with the latest state. It is hoped that researchers in both communities will find the topics relevant and thought provoking. In this volume, the first chapter, written by Professor Colleen Cutler, is a comprehensive account of the theory and estimation of fractal dimension, a topic of central importance in dynamical systems, which has recently attracted the attention of the statisticians. As it is natural to study a stochastic dynamical system within the framework of Markov chains, it is therefore relevant to study their limiting behaviour. The second chapter, written by Professor Kung-Sik Chan, reviews some limit theorems of Markov chains and illustrates their relevance to chaos. The next three chapters are concerned with specific models. Briefly, Chapter Three by Professor Peter Lewis and Dr Bonnie Ray and Chapter Four by Professor Peter Brockwell generalise the class of self-exciting threshold autoregressive models in different directions. In Chapter Three, the new and powerful methodology of multivariate adaptive regression splines (MARS) is adapted to time series data. Its versatility is illustrated by reference to the very interesting and complex sea surface temperature data. Chapter Four exploits the greater tractability of continuous-time Markov approach to discrete-time data. The approach is particularly relevant to irregularly sampled data. The concluding chapter, by Professor Pham Dinh Tuan, is likely to be the most definitive account of bilinear models in discrete time to date.

This volume is the first in the new series Nonlinear Time Series and Chaos. The general aim of the series is to provide a bridge between the two communities by inviting prominent researchers in their respective fields to give a systematic account of their chosen topics, starting at the beginning and ending with the latest state. It is hoped that researchers in both communities will find the topics relevant and thought provoking. In this volume, the first chapter, written by Professor Colleen Cutler, is a comprehensive account of the theory and estimation of fractal dimension, a topic of central importance in dynamical systems, which has recently attracted the attention of the statisticians. As it is natural to study a stochastic dynamical system within the framework of Markov chains, it is therefore relevant to study their limiting behaviour. The second chapter, written by Professor Kung-Sik Chan, reviews some limit theorems of Markov chains and illustrates their relevance to chaos. The next three chapters are concerned with specific models. Briefly, Chapter Three by Professor Peter Lewis and Dr Bonnie Ray and Chapter Four by Professor Peter Brockwell generalise the class of self-exciting threshold autoregressive models in different directions. In Chapter Three, the new and powerful methodology of multivariate adaptive regression splines (MARS) is adapted to time series data. Its versatility is illustrated by reference to the very interesting and complex sea surface temperature data. Chapter Four exploits the greater tractability of continuous-time Markov approach to discrete-time data. The approach is particularly relevant to irregularly sampled data. The concluding chapter, by Professor Pham Dinh Tuan, is likely to be the most definitive account of bilinear models in discrete time to date.

This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.

This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.