1. komponenta

Lecture type	Total
Lectures	45

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES: To adopt the basic terms and results of mathematical statistics.

COURSE DESCRIPTION AND SYLLABUS:
1. Conditional distributions and expectations
2. Multivariate normal distribution
3. Statistical structure
4. Complete and sufficient statistics
5. Uniformly minimum variance unbiased estimators (Rao-Blackwell thm, Lechmann-Scheffe thm)
6. Efficient estimators (regular models, Cramer-Rao thm, Fisher's information)
7. Exponential families
8. Sequences of estimators (consistency, asymptotic normality)
9. Maximum likelihood estimation. Properties of estimators.
10. Multiple linear regression
11. The best linear unbiased estimators (Gauss-Markov conditions)
12. Neyman-Pearson's theory. Uniformly the most powerful tests.
13. Likelihood ratio tests.
14. Examples

1. Fundamentals of Mathematical Statistics, H. T. Nguyen, G. S. Rogers, Springer Verlag, 1989.
2. Uvod u matematičku statistiku, Ž. Pauše, Školska knjiga, Zagreb, 1993.
3. Regression analysis: Theory, Methods, and Applications, A. Sen, M. Srivastava, Springer Verlag, 1990.
4. Theory of Point Estimation, 2nd edition, E. L. Lechmann, G. Casella, Springer Verlag, 1998.
5. Testing Statistical Hypothesis, 2nd edition, E. L. Lechmann, Springer Verlag, 1986.

Izborni predmet 3, 4, 5, 6 - Regular study - Computer Science and Mathematics

Izborni predmet 3, 4, 5, 6 - Regular study - Computer Science and Mathematics