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Core Statistics

Fundamentals of Inference for Parametric Statistical Models

Simon N Wood author

Format:Paperback

Publisher:Cambridge University Press

Published:2nd Apr '15

Currently unavailable, and unfortunately no date known when it will be back

Core Statistics cover

This book serves as an essential introduction to statistical inference and parametric models, ideal for graduate students and quantitative scientists. Core Statistics covers both theory and practical applications.

In Core Statistics, readers are introduced to the essential principles of statistical inference, particularly focusing on parametric models. This book serves as a foundational resource for beginning graduate students and quantitative scientists alike, offering a blend of theoretical insights and practical numerical computation techniques. It aims to equip readers with the necessary tools to navigate the complexities of statistical methods effectively.

The content of Core Statistics encompasses both frequentist maximum likelihood and Bayesian stochastic simulation approaches. By emphasizing general methods that can be applied across various models, the book highlights the common inquiries that both methodologies seek to address. This dual perspective not only enriches the understanding of statistical theory but also enhances the practical application of these concepts in real-world scenarios.

Furthermore, Core Statistics emphasizes critical areas such as inference, modeling, and computational techniques, including the use of the R programming language. It is designed to foster a deeper comprehension of statistical methods, guiding readers on when and why these methods are effective. The book also addresses non-standard situations, making it a valuable resource for those working in diverse fields like ecology, big data, and genomics. Overall, this compact volume provides a lively and engaging introduction to the world of 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

ISBN: 9781107415041

Dimensions: 230mm x 153mm x 15mm

Weight: 400g

258 pages