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Cambridge Series in Statistical and Probabilistic Mathematic #11: Statistical Modelsby A. C. Davison
Synopses & Reviews
Models and likelihood are the backbone of modern statistics and data analysis. The coverage 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. Anthony Davison blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practicioners. Its comprehensive coverage makes this the standard text and reference in the subject.
Gives an integrated development of models and likelihood that blends theory and practice.
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.
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