Statistical Inference (MATH3811)

MATH3811 is a Mathematics Level III course. 

Units of credit: 6

Prerequisites: MATH2831 or MATH2931

Exclusions: MATH3911, MATH5905

Cycle of offering: Term 1

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: The course handout contains information about course objectives, assessment, course materials and the syllabus.

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its policy on plagiarism.

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

The Online Handbook entry contains up-to-date timetabling information.

If you are currently enrolled in MATH3811, you can log into UNSW Moodle for this course.

The aim of the course is to introduce the main ideas and principles behind the parametric and non-parametric inference procedures. The basic methods of inference used throughout Statistics will be discussed rigorously. Students will learn how to choose the appropriate inference procedure and how to perform inference using the chosen procedure.

Coverage of the main parametric and non-parametric and techniques used in statistics. Uniformly minimum variance estimation. Cramer-Rao inequality, Lehmann-Scheffe theorem. Monotone likelihood ratio distributions and uniformly most powerful unbiased tests. Generalised likelihood ratio tests, exact tests and large sample tests. Bayesian point estimation, interval estimation and hypothesis testing. Robustness and bootstrap resampling. Order statistics, goodness of fit, contingency tables. Statistical inference based on ranks. One sample, two sample and k-sample problems, blocked data, independence and association.