Description

This course deals with finite dimensional vector spaces and matrix algebra concepts, generalized inverses of matrices, multivariate normal distribution, distributions of quadratic forms in normal random vectors, estimability and estimation under linear restrictions, testing linear hypotheses.

Prerequirements

An undergraduate course in linear algebra or authorisation by the instructor.

References

• Extending the linear model with R by JJ Faraway. Taylor & Francis, 2006.
• Finite-dimensional vector spaces by PR Halmos. Springer, 1987.
• Linear algebra and linear models by RB Bapat. Universitext, 3rd ed, 2011.
• Linear models by SR Searle and MH Gruber. Wiley, 2nd ed, 2017.
• Linear models in statistics by AC Rencer and GB Schaalje. Wiley, 2008.
• Linear statistical inference and its applications by CR Rao. Willey, 2002.
• Linear statistical models by JH Stapleton. Wiley, 1995.