Fundamentals of Mathematical Statistics

1. Statistical Models. 1. 1. Models and parametrizations. 1. 2. Likelihood, score, and information. 1. 3. Exercises. 2. Linear Normal Models. 2. 1. The multivariate normal distribution. 2. 2. The normal distribution on a vector space. 2. 3. The linear normal model. 2. 4. Exercises. 3. Exponential Families. 3. 1. Regular exponential families. 3. 2. Examples of exponential families. 3. 3. Properties of exponential families. 3. 4. Constructing exponential families. 3. 5. Moments, score, and information. 3. 6. Curved exponential families. 3. 7. Exercises. 4. Estimation. 4. 1. General concepts and exact properties. 4. 2. Various estimation methods. 4. 3. The method of maximum likelihood. 4. 4. Exercises. 5. Asymptotic Theory. 5. 1. Asymptotic consistency and normality. 5. 2. Asymptotics of moment estimators. 5. 3. Asymptotics in regular exponential families. 5. 4. Asymptotics in curved exponential families. 5. 5. More about asymptotics. 5. 6. Exercises. 6. Set Estimation. 6. 1. Basic issues and definition. 6. 2. Exact confidence regions by pivots. 6. 3. Likelihood based regions. 6. 4. Confidence regions by asymptotic pivots. 6. 5. Properties of set estimators. 6. 6. Credibility regions. 6. 7. Exercises. 7. Significance Testing. 7. 1. The problem. 7. 2. Hypotheses and test statistics. 7. 3. Significance and p-values. 7. 4. Critical regions, power, and error types. 7. 5. Set estimation and testing. 7. 6. Test in linear normal models. 7. 7. Determining p-values. 7. 8. Exercises. 8. Models for Tables of Counts. 8. 1. Multinomial exponential families. 8. 2. Genetic equilibrium models. 8. 3. Contingency tables. 8. 4. Exercises.