Statistical models
Author(s)
Bibliographic Information
Statistical models
（Cambridge series on statistical and probabilistic mathematics）
Cambridge University Press, 2008
- : pbk
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Note
"First published 2003. First paperback edition 2008"--T.p. verso
Includes bibliographical references (p. 699-711) and index
Description and Table of Contents
Description
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.
Table of Contents
- 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.
by "Nielsen BookData"