Core Statistics

Core Statistics is a compact starter course on the theory, models, and computational tools needed to make informed use of powerful statistical methods.

Simon N. Wood (Author)

9781107415041, Cambridge University Press

Paperback / softback, published 2 April 2015

258 pages, 43 b/w illus. 2 tables 51 exercises
23 x 15.3 x 1.5 cm, 0.4 kg

'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

Based on a starter course for beginning graduate students, Core Statistics provides concise coverage of the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and Bayesian stochastic simulation while focusing on general methods applicable to a wide range of models and emphasizing the common questions addressed by the two approaches. This compact package serves as a lively introduction to the theory and tools that a beginning graduate student needs in order to make the transition to serious statistical analysis: inference; modeling; computation, including some numerics; and the R language. Aimed also at any quantitative scientist who uses statistical methods, this book will deepen readers' understanding of why and when methods work and explain how to develop suitable methods for non-standard situations, such as in ecology, big data and genomics.

1. Random variables
2. R
3. Statistical models and inference
4. Theory of maximum likelihood estimation
5. Numerical maximum likelihood estimation
6. Bayesian computation
7. Linear models.

Subject Areas: Probability & statistics [PBT], Mathematics [PB], Epidemiology & medical statistics [MBNS], Econometrics [KCH]