STAT 598A, Fall 2008
Analysis of Massive Dependent Data
Tu Thur 3-4:15, University Hall 203
Instructor: Professor Hao Zhang
††††††††††††††††††††††† Office: MATH 536, Ph: 496-9548; Email: email@example.com
Office Hours: Monday and Wednesday 1:30-2:30 and by appointment.
Due to the technological innovation and improved ability to acquire and achieve
data, huge amount of data are collected in many disciplines including
environmental, agricultural and public health studies. For example, the EPA has
thousands of monitoring stations across the
This course covers methods that deal with the additional complexity of modeling and analyzing the massive data, which is attributed to the correlation or dependence in space and time. Some topics include sparse matrices, approximate likelihood-based inferences, covariance tapering, spectral methods, separable space-time covariance functions, and process convolution.
The R language and environment for computing will be used expensively in the class; some packages (ff, bigmemory, fields, spam, sparseM, geoR) will be introduced.
There is no textbook for this course. Lecture notes and designated readings will be distributed.
Grading is based on the following: