| Code |
13930
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| Year |
3
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| Semester |
S1
|
| ECTS Credits |
6
|
| Workload |
TP(60H)
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| Scientific area |
Mathematics
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|
Entry requirements |
NA
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|
Learning outcomes |
To identify, develop and apply statistical models for inference; evaluate statistical inference and decision errors.
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|
Syllabus |
1. Statistical models: exponential models, distributions and empirical moments, exhaustive and complete statistics, Fisher and Kullback information.
2. Parametric estimation: centric and convergent estimators, estimator efficiency, point estimation methods, estimation by regions of confidence.
3. Hypothesis tests: significance and power, convergence of successions of tests, Neyman-Pearson's theorem, multiple hypothesis tests, adjustment tests.
4. Simple linear regression model: least squares estimators, linearity test of the model in the Normal case.
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Main Bibliography |
Cramer, H. (1991) Mathematical Methods of Statistics, Princeton Univ. Press.
Kiefer, J. C. (1987) Introduction to Statistical Inference, Springer-Verlag.
Lehmann, E.L. (1970) Testing Statistical Hypothesis, Wiley, New York.
Schervish, M. (1997) Theory of Statistics, Springer
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Language |
Portuguese. Tutorial support is available in English.
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