General concepts, estimation, prediction, and algorithms

Preface 1. Models 2. Statistical problems and decision theory 3. Statistical information: classical approach 4. Bayesian interpretations of sufficiency, ancillarity and identification 5. Elements of estimation theory 6. Unbiased estimation 7. Maximum likelihood estimation 8. M-estimation 9. Methods of moments and their generalizations 10. Estimation under equality constraints 11. Prediction 12. Bayesian estimation 13. Numerical procedures.