Statistical Computing

I. Syllabus:

The course tackles computational techniques relevant for statistical research and (advanced) applications. The focus will be on developing numerical skills and knowledge useful in the use and application of modern statistical procedures. The computer tutorial gives an introduction to the statistical software R and discusses selected case studies. After completing the course, you will be able to implement complex numerical methods on your own.

  1. Monte Carlo simulation
  2. Optimization (Newton methods, genetic algorithms, EM)
  3. Numerical integration (quadratures, Monte Carlo integration)
  4. Markov Chain Monte Carlo (Metropolis-Hastings, the Gibbs sampler)

 

II. Prerequisites:

  • Calculus, Statistics/Econometrics, Basic programming

 

III. Exam:

  • written exam 
  • you may use a formulary (will be available for download)
  • you can earn some bonus points by solving R assignment

IV. Downloads:

 

V. Materials:

  • Slides will be made available in due time (OLAT)
  • Main textbook: Givens, Geof H., and Jennifer A. Hoeting. Computational statistics. 2nd ed. 2013, Wiley.
  • Mooney, Christopher Z. Monte carlo simulation. 1997, Sage Publications.
  • Dennis, John E., and Robert B. Schnabel. Numerical methods for unconstrained optimization and nonlinear equations. 1996, Siam.
  • Gilks, Walter R., Sylvia Richardson, and David Spiegelhalter, eds. Markov chain Monte Carlo in practice. 1995, CRC press.

 

VI. Lecture:

VII. PC-Tutorial:

Access to the computer lab requires one-time registration with a Stu-Account