Statistical Models

This 2003 book gives an integrated development of models and likelihood that blends theory and practice.

A. C. Davison (Author)

9780521773393, Cambridge University Press

Hardback, published 4 August 2003

738 pages, 119 tables 566 exercises
25.4 x 17.8 x 4 cm, 1.49 kg

' … comprehensive and well written … an excellent reference book for health researchers who are unfamiliar with details of any statistical methodology.' Ramalingam Shanmugam, Texas State University

Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.

1. Introduction
2. Variation
3. Uncertainty
4. Likelihood
5. Models
6. Stochastic models
7. Estimation and hypothesis testing
8. Linear regression models
9. Designed experiments
10. Nonlinear regression models
11. Bayesian models
12. Conditional and marginal inference.

Subject Areas: Probability & statistics [PBT]