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STAT404 Advanced Statistical Inference

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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
Paper code STAT404
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period Semester 1 (On campus)
Domestic Tuition Fees Tuition Fees for 2022 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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STAT 370, MATH 202 and MATH 203

Teaching staff

Matthew Schofield
Ting Wang


Recommended reading:

  • 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
View more information about Otago's graduate attributes.

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

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Semester 1

Teaching method
This paper is taught On Campus
Learning management system