Details of MA4206 (Spring 2023)
| Level: 4 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| MA4206 | Linear Models | Satyaki Mazumder |
| Syllabus |
|---|
| Estimation in Linear Model: Linear statistical models, illustrations, normal equations
and least squares estimators, g-inverse and solution of normal equations, estimability of linear parametric function, Gauss-Markov theorem, error space and estimation space, variances and covariances of BLUEs, estimation of error variance, Fisher-Cochran theorem, distribution of quadratic forms, fundamental theorems of least squares and applications to tests of linear hypotheses, estimation subject to linear restrictions. Generalized Linear Model: Logistic regression, log-linear models, link function, inference for general linear model. |
| Prerequisite |
|---|
| Statistical Inference (MA4107) |
| References |
|---|
| Suggested Texts:
1. Bapat, R.B., Linear Algebra and Linear Models, Springer. 2. Kshirsagar, A.M., A Course in Linear Models, Marcel Dekker. 3. Montgomery, D.C., Peck, E.A. and Vining, G.G., Introduction to Linear Regression Analysis, Wiley. 4. Rao, C.R., Linear Statistical Inference and Its Applications, Wiley. 5. Ryan, T.P., Modern Regression Methods, Wiley- Blackwell. 6. Searle, S.R., Linear Models, Wiley Classic Library, CBS Publishers. 7. Sengupta, D. and Jammalamadaka, S.R., Linear Models : An Integrated Approach, World Scientific |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 2 | Not Allowed |
| 2 | IP | 4 | Not Allowed |
| 3 | IP | 6 | Not Allowed |
| 4 | MP | 2 | Not Allowed |
| 5 | MP | 4 | Not Allowed |
| 6 | MR | 2 | Not Allowed |
| 7 | MR | 4 | Not Allowed |
| 8 | MS | 10 | Elective |
| 9 | MS | 4 | Not Allowed |
| 10 | MS | 6 | Not Allowed |
| 11 | MS | 8 | Elective |
| 12 | RS | 1 | Elective |
| 13 | RS | 2 | Elective |