Foundations of statistical algorithms : with references to R packages / Claus Weihs, Olaf Mersmann, Uwe Ligges

A Chapman & Hall Book.

Includes bibliography and index.

2) Emphasizes recurring themes in all statistical algorithms: Computation, Assessment and Verification, Iteration, Intuition, Randomness, Repetition and Parallelization, and Scalability

3) Discusses two topics not included in other books: systematic verification and scalability

4) Contains examples, exercises, and selected solutions in each chapter

5) Offers access to a supplementary website.

A new and refreshingly different approach to presenting the foundations of statistical algorithms, Foundations of Statistical Algorithms: With References to R Packages reviews the historical development of basic algorithms to illuminate the evolution of today's more powerful statistical algorithms. It emphasizes recurring themes in all statistical algorithms, including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, the book reviews the upcoming challenge of scaling many of the established techniques to very large data sets and delves into systematic verification by demonstrating how to derive general classes of worst case inputs and emphasizing the importance of testing over a large number of different inputs. Broadly accessible, the book offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website. After working through the material covered in the book, readers should not only understand current algorithms but also gain a deeper understanding of how algorithms are constructed, how to evaluate new algorithms, which recurring principles are used to tackle some of the tough problems statistical programmers face, and how to take an idea for a new method and turn it into something practically useful. It Features:1) Covers historical development as this is clarifies the evolution of more powerful statistical algorithms

Reviewing the historical development of basic algorithms to illuminate the evolution of today's more powerful statistical algorithms, this comprehensive textbook emphasizes recurring themes in all statistical algorithms including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, it touches on topics not usually covered in similar books, namely, systematic verification and the scaling of many established techniques to very large databases. Broadly accessible, it offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website.