Models and likelihood are the backbone of modern statistics. This 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, missingdata, 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. INDICE: 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.
- ISBN: 978-0-521-73449-3
- Editorial: Cambridge University
- Encuadernacion: Rústica
- Páginas: 736
- Fecha Publicación: 01/06/2008
- Nº Volúmenes: 1
- Idioma: Inglés