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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.
-Gonçalves E. e N. Mendes-Lopes, N. (2003). Estatística - Teoria Matemática e Aplicações, Escolar Editora.
-Bolfarine, H., Sandoval, M.C. (2000). Introdução à Inferência EstatÍstica.
-Kiefer, J. C. (1987) Introduction to Statistical Inference, Springer-Verlag.
-Mood, A., Graybill, F. e Boes, D. (1974). Introduction to the Theory of Statistics. McGraw-Hill International Editions.
-Murteira, B.J.F. (1980). Probabilidades e Estatística, Vol. 2. Editora McGraw-Hill de Portugal, Lda.
-Murteira, B.J.F. (1990). Probabilidades e Estatística, Vol. 2 (2a. edição). Editora McGraw-Hill de Portugal, Lda.
-Rohatgi, V.K (1976). An Introduction to probability theory and mathematical statistics. New York: John Wiley.
-Tong, Y.L. (1990). The Multivariate Normal Distribution. Springer-Verlag.
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