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This unique book delivers an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. The book is unique in its detailed coverage of fundamental topics. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. There is no other book in large sample theory that matches this book in coverage, exercises and examples, bibliography, and lucid conceptual discussion of issues and theorems.
This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year long graduate course...
In this book, Kalyan Sanyal reviews the traditional notion of capitalism and propounds an original theory of capitalist development in the post-colonial context. In order to substantiate his theory, concepts such as primitive accumulation, governmentality and post-colonial capitalist formation are discussed in detail. Analyzing critical questions from a third world perspective such as: Will the integration into the global capitalist network bring to the third world new economic opportunities? Will this capitalist network make the third world countries an easy prey for predatory multinational corporations? The end result is a discourse, drawing on Marx and Foucault, which envisages the post-colonial capitalist formation, albeit in an entirely different light, in the era of globalization.
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i...
Introductory Probability is a pleasure to read and provides a fine answer to the question: How do you construct Brownian motion from scratch, given that you are a competent analyst? There are at least two ways to develop probability theory. The more familiar path is to treat it as its own discipline, and work from intuitive examples such as coin flips and conundrums such as the Monty Hall problem. An alternative is to first develop measure theory and analysis, and then add interpretation. Bhattacharya and Waymire take the second path.
This imaginative and ambitious book rethinks the nature of capitalism. Lippit, a leading heterodox economist in the USA, here delivers a comparative study of different forms of capitalism. He first critically examines the three main capitalist prototypes: * the Anglo-American, market-driven version* the welfare-state capitalism of continental Europe* the state-led capitalism of East Asia.After investigating their various intricacies, he then goes on to analyze the common weaknesses of each different strand. A provocative and stimulating read, this book wi.
This book constitutes the refereed proceedings of the 8th International Workshop on Algorithms and Models for the Web-Graph, WAW 2011, held in Atlanta, GA, in May 2011 - co-located with RSA 2011, the 15th International Conference on Random Structures and Algorithms. The 13 revised full papers presented together with 1 invited lecture were carefully reviewed and selected from 19 submissions. Addressing a wide variety of topics related to the study of the Web-graph such as theoretical and empirical analysis, the papers feature original research in terms of algorithmic and mathematical analysis in all areas pertaining to the World-Wide Web with special focus to the view of complex data as networks.
This book constitutes the refereed proceedings of the 11th Annual European Symposium on Algorithms, ESA 2003, held in Budapest, Hungary, in September 2003. The 66 revised full papers presented were carefully reviewed and selected from 165 submissions. The scope of the papers spans the entire range of algorithmics from design and mathematical analysis issues to real-world applications, engineering, and experimental analysis of algorithms.
These volumes present a selection of Erich L. Lehmann’s monumental contributions to Statistics. These works are multifaceted. His early work included fundamental contributions to hypothesis testing, theory of point estimation, and more generally to decision theory. His work in Nonparametric Statistics was groundbreaking. His fundamental contributions in this area include results that came to assuage the anxiety of statisticians that were skeptical of nonparametric methodologies, and his work on concepts of dependence has created a large literature. The two volumes are divided into chapters of related works. Invited contributors have critiqued the papers in each chapter, and the reprinted group of papers follows each commentary. A complete bibliography that contains links to recorded talks by Erich Lehmann – and which are freely accessible to the public – and a list of Ph.D. students are also included. These volumes belong in every statistician’s personal collection and are a required holding for any institutional library.