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A Handbook of Small Data Sets
This book should be of interest to statistics lecturers who want ready-made data sets complete with notes for teaching.
Mining of Massive Datasets
Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Learning from Imbalanced Data Sets
This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing metho...
Algorithms and Data Structures for Massive Datasets
In Algorithms and Data Structures for Massive Datasets, you'll discover methods for reducing and sketching data so it fits in small memory without losing accuracy, and unlock the algorithms and data structures that form the backbone of a big data system. Data structures and algorithms that are great for traditional software may quickly slow or fail altogether when applied to huge datasets. Algorithms and Data Structures for Massive Datasets introduces a toolbox of new techniques that are perfect for handling modern big data applications. In Algorithms and Data Structures for Massive Datasets, you'll discover methods for reducing and sketching data so it fits in small memory without losing accuracy, and unlock the algorithms and data structures that form the backbone of a big data system. Filled with fun illustrations and examples from real-world businesses, you'll learn how each of these complex techniques can be practically applied to maximize the accuracy and throughput of big data processing and analytics. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Slaying the Dragon
Dungeons & Dragons. It’s the fantasy role-playing game first conceived over fifty years ago by the now-legendary company TSR ,which has enthralled millions of devoted gamers around the world for generations. It’s a test of skill, intelligence, audacity, and survival. But no D&D game ever played could compare to the stunning behind-the-scenes melee for power and dominance that was the true story of TSR. Slaying the Dragon chronicles the rise and fall of TSR (Tactical Studies Rules), how the brilliant and wild minds of the legendary Gary Gygax and his co-creator Dave Arneson gave birth to a game that would capture the imagination of outsiders and underdogs throughout the world. From its hu...
Learning SAS by Example
Learn to program SAS by example! Learning SAS by Example, A Programmer’s Guide, Second Edition, teaches SAS programming from very basic concepts to more advanced topics. Because most programmers prefer examples rather than reference-type syntax, this book uses short examples to explain each topic. The second edition has brought this classic book on SAS programming up to the latest SAS version, with new chapters that cover topics such as PROC SGPLOT and Perl regular expressions. This book belongs on the shelf (or e-book reader) of anyone who programs in SAS, from those with little programming experience who want to learn SAS to intermediate and even advanced SAS programmers who want to lear...
Introduction to Econometrics
Taking a modern approach to the subject, this text provides students with a solid grounding in econometrics, using non-technical language wherever possible.
Handbook of Massive Data Sets
The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications. With advances in computer and information technologies, many of these challenges are beginning to be addressed by diverse inter-disciplinary groups, that indude computer scientists, mathematicians, statisticians and engineers, working in dose cooperation with application domain experts. High profile applications indude astrophysics, bio-technology, demographics, finance, geographi cal information systems, government, medicine, telecommunications, the environment and the internet. John R. Tucker of the Board...
Inference and Asymptotics
Our book Asymptotic Techniquesfor Use in Statistics was originally planned as an account of asymptotic statistical theory, but by the time we had completed the mathematical preliminaries it seemed best to publish these separately. The present book, although largely self-contained, takes up the original theme and gives a systematic account of some recent developments in asymptotic parametric inference from a likelihood-based perspective. Chapters 1-4 are relatively elementary and provide first a review of key concepts such as likelihood, sufficiency, conditionality, ancillarity, exponential families and transformation models. Then first-order asymptotic theory is set out, followed by a discus...
