Linear Models and GLM
Instructor: Antar Bandyopadhyay
(Email: antar (at) isid (dot) ac (dot) in
Office: 208 Faculty Building).
Class Time: Tu Th 11:30 - 13:00, F 14:00 - 15:00
in Class Room 23.
Instructor's Office Hours: F 15:30 - 17:30 at Room # 208.
Course Duration: January 7 -- April 18, 2008.
Mid-Term Examination Date: February 28, 2008 (Thursday)
Final Examination Date: April 23, 2008 (Wednesday)
- Linear statistical models/Gauss-Markov Models.
- Least square estimation, Estimable linear functions, Normal equations,
Best Linear Unbiased Estimates (BLUEs).
- Gauss-Markov Theorem.
- Variance of BLUEs.
- Normality assumption of error. Maximum likelihood estimation vs
Least square estimation/MLE vs BLUE. MVUE vs BLUE.
- Estimation of error variance. Degrees of freedom.
Fundamental Theorems of Least Square.
Testing of linear hypotheses.
- Various different linear models and ANOVA.
- One way classification.
- Two way classification with and without interactions. Equal and
unequal number of observations per cell. Tukey's One Degree of
- Nested classification model.
- Three way classification, various different types of interactions.
- Multiple regression.
- Multiple comparisons.
- Analysis of Covariance (ANCOVA).
- Random Effect Models.
- Basic idea of Log-Linear Models
and Logistic regression (will be done by Dr. T. Krishnan).
- Some basics of Generalized Linear Models (GLMs),
linear predictor, link function, canonical link function, deviance.
Maximum likelihood estimation, iteratively re-weighted least square
algorithm. Goodness of fit test. Examples including logit and
- Liner Algebra (at the level of Finite Dimensional Vector Spaces,
by P. R. Halmos).
- Basic Statistical Inference (at the level of
Mathematical statistics by Peter J. Bickel and Kjell A. Doksum).
- Plane Answers to Complex Questions by R. Christensen.
- Linear Statistical Inference by C. R. Rao.
- Linear Models, An Integrated Approach by
D. Sengupta and S. R. Jammalamadaka.
- Log-Linear Models by R. Christensen.
- Generalized Linear Models by P. McCullagh and J. A. Nelder.
- Assignments: 15% of the total credit.
- Two Quizes: 10% of the total credit.
- Midterm Exam: 25% of the total credit.
- Final Exam: 50% of the total credit.
- There will be a total of 6 or 7 sets of homework assignments each
carrying a total of 20 points.
- The assignments will be given in class every alternate Fridays,
starting from the first week of the semester. That is, the first assignment
will be given on January 11, 2008, the second assignment will
be given on January 25, 2008, and so on.
- An assignment given on a Friday will cover the topics discussed in
the lectures in that week and the following week. For example,
the first assignment dated January 11, 2008, will cover lecture materials
of the first and the second week of the semester.
- You DO NOT need to submit the assignments. They will not be
graded. Instead, on every Friday, starting from the second week of the
course, we will have tutorials where the students will be asked to present
their solution on the board. Grades will be given based on the
- Note, not every one will be asked to present a solution in every
tutorial. The selection will be done randomly and over the whole
semester it will cover (almost) uniformly all the students.
- If you are not present in a tutorial and your name came in the selection
then you will lose a grade for that. But don't worry you will have enough
- Click here
for downloading the assignments.
- There will be two quizes as surprised tests given in the
class. This means there shall be no pre-scheduling. A quiz will cover
materials done in the lectures given in the weeks prior to it.
- There will be NO supplementary quiz given for any student who
may miss a quiz for whatsoever reason. If you miss one then do not worry
try doing well in the other.
- Quiz # 1
(answers to the Quiz # 1).
- Quiz # 2
(answers to the Quiz # 2).
Last modified April 21, 2008.