Description

Mathematical Statistics is a graduate level statistics course including topics from mathematical statistics and statistical inference. This course covers preliminary concepts in mathematical statistics, unbiased estimation and uniformly minimum variance unbiased estimators, maximum likelihood estimation, hypothesis testing, interval estimation, Bayesian methods and asymptotic evaluations. The course is a mandatory course in M.Sc. and Ph.D. programmes in Statistics, and usually offered in the Fall semester.

Prerequisites

An undergraduate background in probability, mathematical statistics and statistical inference is expected.

Basic references

• Statistical inference, 2nd edition by G. Casella and R.L. Berger. Pacific Grove, CA: Duxbury, 2002.
• Theory of point estimation by E.L. Lehmann and G. Casella. Springer, 2006
• Testing statistical hypotheses by E.L. Lehmann and J.P. Romano. Springer, 2006.
• Theoretical statistics by D.R. Cox and D.V. Hinkley. CRC Press, 1979.
• Introduction to mathematical statistics, 8th edition by R.V. Hogg, J.W. McKean and A.T. Craig. Pearson, 2018.