| Unit 1- Consistency and asymptotic normality (CAN) of real and vector parameters. Invariance of consistency under continuous transformation. Invariance of CAN estimators under differentiable transformations, generation of CAN estimators using central limit theorem. | View Link |
| Unit 2- Method of moments, method of maximum likelihood, Special cases such as exponential class of densities and multinomial distribution, Cramer-Huzurbazar theorem, method of scoring. | View Link |
| Unit 3- Tests based on MLEs. Likelihood ratio tests, asymptotic distribution of log likelihood ratio, Wald Test, Score Test, locally most powerful tests. Applications to categorical data analysis, three dimensional contingency tables, Pearson’s chi-square test and LR test. | View Link |
| Unit 4- Asymptotic comparison of tests. Asymptotic Relative Efficiency (Pitman’s). Introduction to Nonparametric Methods, one sample tests | View Link |
| Unit 5- chapter 5 | View Link |
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