Oct 28 - part 4
Nov 23
Syllabus:
Random process data analysis: frequency- and time-side analysis of ordinary time series, stationarity and non-stationarity, parametric and nonparametric (ARIMA, GARCH, ...), Markov chains, point processes, spatial processes, spatial-temporal processes, ...
These topics will be presented in a comparative fashion
Classes: Tu Th 2:00 - 3:30 in 332 Evans
Lab Section: Fri 14:00 - 16:00 in 332 Evans.
Lab web site
Course Homepage: www.stat.berkeley.edu/~brill/Stat248
Supplimentary texts: D. R. Brillinger, Time Series: Data Analysis and Theory, SIAM.
P. Guttorp, Stochastic Modelling of Scientific Data, Chapman and Hall.
web partial version
Prerequisites: Statistics 101&102 or 134&135 or equivalents.
The course work will be directed towards the students preparing an analysis
of pertinent scientific data using the methods covered in the course.
The Lab will be directed to teaching the pertintent material concerning the statistical package R, particularly the time series functions, and to helping the students with realizing their projects.
The grade will come from a combination of a Project Proposal and an Independent Project.
The Proposal is due 19 October at class and the Project is due by 14:10 9 December in the class room.
17 January 2012
brill@stat.Berkeley.EDU
