The course aims to provide students with adequate theoretical background and mathematical   tools to access the literature on biostatistics methodologies and applications. Topics covered include: stochastic convergence; delta method; moment estimator; M- and Z- estimators; Bayes   procedures; likelihood ratio tests; chi-square tests; nonparametric estimation; semiparametric inference and efficiency; permutation tests; Bootstrap methods. Selected topics such as empirical likelihood, U-statistics, Bayesian/MCMC methods, EM algorithm, counting process methods in survival analysis, functional data analysis, shape-constrained inference, will also be discussed.

Examination questions are designed to see how well students have achieved the learning objectives and acquired the requisite techniques for problem-solving.

To pass the course, students are required to obtain a minimum of 30% in continuous assessment and a minimum of 30% in the examination.