Mathematical Statistics
Course Code
MA3081
Number of Credits
4
Semester
Course Type
Study Material
| Study Material | Depth |
|---|---|
| Distribution of sample parameters for mean and variance (X-bar and sample variance) | Expert |
| Statistical series, limit distribution, central limit theorem | Expert |
| Convergence of random variable series and types of convergence (convergent in probability and convergent in distribution) | Expert |
| Converges in probability and Chebyshev's inequality | Expert |
| Converges in the distribution and limit of the moment generating function | Expert |
| Parameter estimation methods: maximum likelihood and method of moments and properties of estimators | Expert |
| Interval estimators for mean and variance | Expert |
| Interval estimator for difference of two means and ratio of two variances, Point and interval estimator of Bayes method | Expert |
| Definition of hypothesis testing, best critical region, Neyman-Pearson argument | Expert |
| Likelihood ratio test and UMPT | Expert |
| Sufficient Statistics via Neyman Factorisation and exponential distribution classes | Expert |
| Properties of unbiased and efficient estimators, determination of MVUE estimator by Rao-Blackwell's Razor | Expert |
| Completeness and uniqueness of estimator via Rao-Cramer Inequality | Expert |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Basic skills to learn statistical distributions that support population parameter estimation. |
| CPMK 2 | Able to determine estimated population parameters through correct mathematical steps. |
| CPMK 3 | Basic ability to learn statistical distributions that support population parameter estimation. Able to determine estimated population parameters through correct mathematical steps. |
| CPMK 4 | Able to determine estimated population parameters through correct mathematical steps. |
Learning Method
- Lectures, collaborative learning, and group discussions.
Learning Modality
- Offline, synchronized, self-paced
Assessment Methods
- Quizzes, midterms, final exams, and assignments