Week 6: Deriving tests of Hypothesis concerning parameters in uniform distributions
Course Material
- Week 1: Introduction of Tests of Hypotheses: Simple and composite hypotheses, critical regions.
- Week 2: Neyman-Pearson Lemma, Statement, proof and its examples
- Week 3: Power functions, most powerful tests and uniformly most powerful tests
- week 4: Deriving tests of Hypothesis concerning parameters of Normal distribution
- Week 5: Deriving tests of Hypothesis concerning parameters in exponential, gamma with examples
- Week 6: Deriving tests of Hypothesis concerning parameters in uniform distributions
- Week 7: Randomized Tests and Unbiased tests
- Week 8: Maximum Likelihood estimation and examples with different distributions
- Week 9: Generalized Likelihood Ratio test
- Week 10: Generalized Likelihood ratio tests for Location parameter of Normal distribution when dispersion is unknown as well as known
- Week 11: Likelihood ratio tests and their asymptotic properties
- Week 12: Wilks Theorem and its applications
- Week 13: Sequential Tests: SPRT and its properties
- Week 14: Goodness of fit of Wilks theorem and Power of chi square test by using R language and Central Limit Theorem , Statement and application by using R language
- Week 15: Interval Estimation: Pivotal and other methods of finding confidence interval
- Week 16: Shortest confidence interval, optimum confidence interval. Bayes’ Interval estimation
- Chapters 16
- Department Statistics
- Teacher
Dr. Hafiz Zafar Nazir
