Course Content
WeekTopicsStudy Materials _ocw_rs_drs_yontem
1 Basic concepts of multivariate statistics Source reading
2 Matrix theory for multivariate statistical analysis Source reading
3 Matrix theory for multivariate statistical analysis Source reading
4 Mean vector, variance-covariance matrix, correlation matrix Source reading
5 Probability density function, marginal probability density function, conditional distribution and statistical independency for multivariate distributions Source reading
6 Probability density function, characteristic functions, moments, moment generating function and parameter estimates Source reading
7 Maximum likelihood estimators for population parameters Source reading
8 Mid-term exam Review the topics discussed in the lecture notes and sources
9 Marginal normal distribution, conditional normal distribution Source reading
10 Distribution of linear relations, independency of subvector variables Source reading
11 Obtaining the parameters given the density function Source reading
12 Multivariate test methods (likelihood ratio test) Source reading
13 Multivariate test methods (composition-intersection test) Source reading
14 Test on mean vectors Source reading
15 Test on covariance matrices, Testing hypothesis with statistical package programs. Source reading
16-17 Final exam Review the topics discussed in the lecture notes and sources

Recommended or Required Reading
Textbook
Additional Resources
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