Degree Level Course
Linear Statistical Models
To introduce linear statistical models and their applications in estimation and testing. The course will illustrate concepts with specific examples, data sets and numerical exercises using statistical package R.
by Siva Athreya
Course ID: BSMA3012
Course Credits: 4
Course Type: Elective
Pre-requisites: None
Course structure & Assessments
12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.
| WEEK 1 | Review of Estimation, Hypothesis Testing |
| WEEK 2 | Review of working with R-package |
| WEEK 3 | Least square estimation, estimable linear functions |
| WEEK 4 | Normal equations |
| WEEK 5 | Best Linear Unbiased Estimates (BLUEs). |
| WEEK 6 | Gauss-Markov Theorem. |
| WEEK 7 | Degrees of freedom. Fundamental Theorems of Least Square. |
| WEEK 8 | Testing of linear hypotheses. |
| WEEK 9 | One-way and two-way classification models |
| WEEK 10 | ANOVA and ANCOVA. |
| WEEK 11 | Nested models. Multiple comparisons |
| WEEK 12 | Introduction to random effect models. |
Prescribed Books
The following are the suggested books for the course:
Plane Answers to Complex Questions The Theory of Linear Models, Springer by R. Christensen.
Linear Statistical Inference by C. R. Rao.
About the Instructors
Siva Athreya received his Bachelor of Science (Honours) Mathematics from St. Stephen’s College, New Delhi, India in 1991. After obtaining a Master of Statistics from Indian Statistical Institute, Kolkata, India in 1993 he obtained his PhD in Mathematics from the University of Washington, Seattle, U.S.A. in 1998. His research interests include: Stochastic Analysis (Stochastic Partial Differential Equations and Stochastic Differential Equations); Random walks among mobile traps; Random Graphs; Tree-valued Processes; Computational Epidemiology. He currently serves as Editor-in-Chief: Electronic Communications in Probability.
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