This course introduces concepts in statistical inference, the application of procedures for drawing conclusions from data while allowing for random variation. The emphasis is on likelihood-based methods for inference. Topics include estimators, estimator behaviour, likelihood functions and derivation of maximum likelihood estimators. Important concepts such as type I and type II errors, p-values and confidence intervals are also discussed.
Availability2018 Course Timetables
- Semester 1 - 2018
On successful completion of the course students will be able to:
1. Understand the properties of desirable estimators and methods for assessing estimator behaviour;
2. Understand the concept of statistical likelihood and its use in parameter estimation;
3. Derive maximum likelihood estimators for parameters of common probability distributions;
4. Understand the properties of maximum likelihood estimators;
5. Understand and apply the principles of hypothesis testing;
6. Understand type I and type II statistical errors.
This course presents an overview of statistical inference with an emphasis on maximum likelihood approaches. The properties of desirable estimators are reviewed together with methods for assessing estimator behaviour and the large-sample properties of estimators. Students are introduced to the concept of likelihood as a method for assessing support for different parameter values. Experience will be gained in applying likelihood-based methods for purposes including deriving estimators and expressions for asymptotic variance as well as undertaking inference. Students will study key statistical concepts such as type I and type II errors, p-values and confidence intervals. Students will also learn about the philosophical and practical differences between frequentist and Bayesian methods.
Written Assignment: Assignment 1
Written Assignment: Assignment 2
Written Assignment: Assignment 3
Written Assignment: Assignment 4
Self-Directed 6 hour(s) per Week for Full Term
Suggest 8-12 hours per week time commitment (guide only).