Core Statistics

"The author keeps this book concise by focusing entirely on topics that are most relevant for scientific modeling via maximum likelihood and Bayesian inference. This makes it an ideal text and handy reference for any math-literate scientist who wants to learn how to build sophisticated parametric models and fit them to data using modern computational approaches. I will be recommending this well-written book to my collaborators."
Murali Haran, Pennsylvania State University

"Simon Wood has written a must-read book for the instructor, student, and scholar in search of mathematical rigor, practical implementation, or both. The text is relevant to the likelihoodist and Bayesian alike; it is nicely topped off by instructive problems and exercises. Who thought that a core inference textbook needs to be dry?"
Geert Molenberghs, Universiteit Hasselt and KU Leuven, Belgium

"Simon Wood's book Core Statistics is a welcome contribution. Wood's considerable experience in statistical matters and his thoughtfulness as a writer and communicator consistently shine through. The writing is compact and neutral, with occasional glimpses of Wood's wry humour. The carefully curated examples, with executable code, will repay imitation and development. I warmly recommend this book to graduate students who need an introduction, or a refresher, in the core arts of statistics."
Andrew Robinson, University of Melbourne

'This is an interesting book intended for someone who has already taken an introductory course on probability and statistics and who would like to have a nice introduction to the main modern statistical methods and how these are applied using the R language. It covers the fundamentals of statistical inference, including both theory in a concise form and practical numerical computation.' Vassilis G. S. Vasdekis, Mathematical Reviews