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This book can be roughly divided in three parts. In the first part (comprising the first eight chapters) the author lays out the foundation for the mixed effects model. The second part of the book (comprised by Chapter 9) discusses methods of model diagnostics and influential analysis, whereby influence is the equivalent of sensitivity analysis, in that they both aim to determine the effect of a small (or infinitesimal) perturbation over data or a model. The third part of the book deals with applications, with Chapter 10 covering tumor growth, and Chapters 11 and 12 covering statistical shape and image analysis. The book is written in a way that will satisfy both a statistician and a computer scientist. Helpful summary points are provided at the end of each chapter. In addition, the book cites a vast range of references, which make it possible for anyone wanting further information on any subject. One gripe is that the book does not include exercises at the end of each chapter, which could have been ideal if one were to adopt it for a graduate-level course.