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Online course materials for MATH38091

Statistical Computing


Unit code: MATH38091
Credit Rating: 10
Unit level: Level 3
Teaching period(s): Semester 1
Offered by School of Mathematics
Available as a free choice unit?: N

Requisites

Prerequisite

Additional Requirements

Please note

Students are not permitted to take more than one of MATH38091 or MATH48091 for credit in the same or different undergraduate year.

Students are not permitted to take MATH48091 and MATH68091for credit in an undergraduate programme and then a postgraduate programme.

Note that MATH68091 is an example of an enhanced level 3 module as it includes all the material from MATH38091

When a student has taken level 3 modules which are enhanced to produce level 6 modules on an MSc programme taken within the School of Mathematics, then they are limited to a maximum of two such modules (with no alternative arrangements available otherwise)

Aims

To introduce the student to computational statistics, both the underlying theory and the practical applications.

Overview

Computers are an invaluable tool to modern statisticians. The increasing power of computers has greatly increased the scope of inferential methods and the type of models which can be analysed. This has led to the development of a number of computationally intensive statistical methods, many of which will be introduced in this course.

Learning outcomes

On successful completion of this course unit students will be able to

  • appreciate the usefulness of computational methods in modern statistics;
  • understand the basic ideas underpinning the theory;
  • be able to apply the methodology to standard problems.

Assessment methods

  • Other - 50%
  • Written exam - 50%

Assessment Further Information

  • Three coursework projects: 50%
  • End of semester written examination (1.5 hours): 50%

Syllabus

  •  Introduction [1]
  • Simulating random variables: inversion of the cdf; rejection sampling; transformations; ratio of uniforms. [4
  • Monte Carlo integration [1]
  • Variance Reduction: importance sampling; control variates. [2]
  • Nonparametric bootstrap methods; the Jackknife. [6]
  • Nonparametric density estimation: the histogram; kernel density estimation. [4]
  • Nonlinear regression: model specification; least squares estimation; Gauss-Newton algorithm. [2]
  • Nonparametric regression: moving average estimator. [2]

Recommended reading

  • Rizzo, M.  Statistical Computing with R.  Chapman & Hall
  • Ripley, B.D.  Stochastic Simulation.  Wiley.
  • Efron, B. and Tibshirani, R. An introduction to the bootstrap.  Chapman & Hall
  • Jones, M. and Wand, M.  Kernel smoothing.  Chapman & Hall

Feedback methods

Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding.  Coursework also provides an opportunity for students to receive feedback.  Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.
 

Study hours

  • Lectures - 16 hours
  • Tutorials - 16 hours
  • Independent study hours - 68 hours

Teaching staff

Peter Foster - Unit coordinator

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