A core theory paper, examining the foundations of statistical methods of inference in both frequentist and Bayesian settings. It will include classical and modern computer-age methods.
|Paper title||Advanced Statistical Inference|
|Teaching period||Semester 1 (On campus)|
|Domestic Tuition Fees (NZD)||$1,206.91|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- STAT 370, MATH 202 and MATH 203
- Teaching staff
- Casella, G., & Berger, R. L. (2002). Statistical inference (2nd edition). Pacific Grove, CA: Duxbury.
- Gelman, A., Carlin, J., Stern, H., and Rubin, D.B. (2003) Bayesian Data Analysis. Second Edition.
- Robert, C. P. (2007) The Bayesian Choice. Second edition.
- Gelman, A. and Hill, J. (2006) Data Analysis Using Regression and Multilevel/Hierarchical Models.
- Albert, J. (2009) Bayesian Computation with R.
- Link, W. A. and Barker, R. J. (2010) Bayesian Inference with Ecological Applications.
- G. Young & R. Smith. Essentials of Statistical Inference.
- B. Efron & T. Hastie. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science.
- Graduate Attributes Emphasised
Communication, Critical Thinking, Lifelong learning, Information Literacy, Research, Teamwork
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- Learning Outcomes
Students who successfully complete the paper will:
- Understand the principles of Bayesian and frequentist inference methods, the theoretical properties and the proofs that were given for these methods
- Apply the methods to particular situations or statistical models that are not covered in this paper
- Choose appropriate methods of inference to tackle real problems
- Understand a scientific article that uses methods covered in this paper