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.

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