Weihs, Claus; Mersmann, Olaf ; Ligges, Uwe

"My main take away is that these authors spend a lot of time thinking about issues that I never think about. They argue strongly that I, as a statistician, should think about them more, and I find their argument compelling. I certainly enjoyed the various flashes of insight into computation I had as I read their book. ... The book's case studies ... are incredibly detailed and deep, far beyond the case studies typically used to illustrate these methods. ... I greatly enjoyed the overall arc of the book and found it quite compelling. ... a nice book to have on the shelf in case you find yourself suspicious about something computational, or want to find a case study illustrating some topic in computation." -Luke W. Miratrix, Journal of the American Statistical Association, March 2015 "... it provides the necessary skills to construct statistical algorithms and hence to contribute to statistical computing. And I wish I had the luxury to teach from Foundations of Statistical Algorithms to my graduate students ... a rich book that should benefit a specific niche of statistical graduates and would-be-statisticians, namely those ready to engage into serious statistical programming. It should provide them with the necessary background, out of which they should develop their own tools." -Christian Robert on his blog, February 2014 "The book is suitable for readers who not only want to understand current statistical algorithms, but also gain a deeper understanding of how the algorithms are constructed and how they operate. It is addressed first and foremost to students and lecturers teaching the foundations of statistical algorithms." -Ivan Krivy, Zentralblatt MATH 1296

• Introduction. Computation. Verification. Iteration. Deduction of Theoretical Properties. Randomization. Repetition. Scalability and Parallelization. Bibliography. Index.