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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.

    About this paper

    Paper title Advanced Statistical Inference
    Subject Statistics
    EFTS 0.1667
    Points 20 points
    Teaching period Semester 1 (On campus)
    Domestic Tuition Fees ( NZD ) $1,240.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    STAT 370, MATH 202 and MATH 203

    Teaching staff

    Associate Professor Matthew Schofield

    Associate Professor 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


    Semester 1

    Teaching method
    This paper is taught On Campus
    Learning management system


    Stream Days Times Weeks
    A1 Monday 14:00-14:50 9-13, 15-22
    Wednesday 14:00-14:50 9-13, 15-22
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