**Instructor:**Professor Jiayang Sun, Yost 326, 368-0630, jsun at case.edu**Office Hours:**4:15-5pm TTH (subject to change)**TA:**Mr. Xiaosong Li, Yost 339, 368-2135, Xiaosong.li1 at case edu**Office Hours:**2-4pm MW**Other TAs' Hours:**http://stat.case.edu/tutor-schedule.pdf**Course Overview:**Statistical computing is an essential part of modern statistical training, as it touches on almost every aspect of statistical theory and practice. This course covers some basic elements of statistical computing (numerical computation, seminumerical computation, symbolic and graphical computation) and some special topics. A*tentative*list of topics include:- Introduction to computational statistics
- Errors
- Computer arithmetic
- Some tricks including recurrence relations

- Seminumerical computation - Stochastic simulations
- Examples of simulation
- Pseudo-random deviate
- Non-uniform variate generation
- Variance reduction methods
- Monte Carlo Methods for Statistics -> Special Topics

- Numerical computation
- Numerical linear algebra and linear regressions
- Integration and approximations
- Optimization and root finding

- Graphical and symbolic computation
- Graphical statistical methods
- Applications in R/Splus, Maple and Matlab

- Special Topics
- Jackknife and Bootstrap
- Gibbs sampling and Markov Chain Monte Carlo
- EM algorithms, Laplace Approximations ...
- Movies and special effects webpages?

- Introduction to computational statistics
**Prerequisite:**Stat 425 and some knowledge about R/Splus and one of the following three programming languages: Fortran, C/C++ and Pascal.**Text Books:**No textbook is required since no single text coversall our topics. The course materials will be drawn from following excellent, **recommended books:**- Lange, K. (2010), Numerical Analysis for Statisticians, Springer, ISBN: 978-1-4419-5944-7, DOI 10.1007/978-1-4419-5945-4, 2nd edition.(Book Info)
- Ripley, B. D. (2006), Stochastic Simulation, Reprint of 1987 original, Wiley. ISBN: 978-0-470-00960-4.
**Other References:**- Givens, G. H., Hoeting, J.A. (2005), Computational Statistics, Wiley. ISBN: 0-471-46124-5. ( Code and Data sets)
- Jones,O., Mallardet, R. and Robinson, A. (2009), Introduction to Scientific Programming and Simulation Using R, Chapman & Hall/CRC. ISBN: 9781420068726
- Martinez, W. L. and Martinez, A. R. (2007), Computational Statistics Handbook with MATLAB, Second Edition, Chapman & Hall/CRC Computer Science and Data Analysis. ISBN: 9781584885665.
- Gentle, J. (2004), Elements of Computational Statistics, Springer. ISBN: 0-387-95489-9.
- Gentle, J. (2005), Random Number Generation and Monte Carlo Methods, Springer, 2nd edition. ISBN 0-387-00178-6.
- Stewart, G. W. (1993), Afternotes on Numerical Analysis. (Do not print all pages!)
- Press et. al. (1992, 1996, 2007), Numerical Recipes: The art of scientific computing, Cambridge University Press: Cambridge. 2nd Edition (1992). 3rd edition (2007). (Do not print all pages!)

- Gentle, J., Ha"rdle, W. and Mori, Y (2004), Handbook of computational statistics, Concepts and methods, Springer-Verlag, Berlin. ISBN 3-540-40464-362-06
- Braun, W.J. and Murdoch, D.J. (2007), A First Course in Statistical Programming with R, Cambridge University Press.
- Matloff, N. (2011),The Art of R Programming: a tour of statistical software design. Publisher: William Pollock.
- Thisted, R. (1988), Elements of Statistical Computing. Chapman and Hall.

**Grades:**- The assessment will be based on
*homework assignments*(50%),*a presentation*(10%) and a*final examination*(40%).**Class Webpage:**http://sun.cwru.edu/~jiayang/427/