Statistical models
著者
書誌事項
Statistical models
（Cambridge series on statistical and probabilistic mathematics）
Cambridge University Press, 2003
- : hardback
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注記
Includes bibliographical references (p. 699-711) and index
内容説明・目次
内容説明
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.
目次
- 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.
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