Lectures: Thursday, 4:30-7:10pm, Engineering Building, room 1108
Some of the lectures will be based on notes posted on this website. Some lectures will be accompanied only by notes written on the board.
If you send email to the instructor, please put "CSI 771" or "STAT 751" in the subject line.
The general description of the course is available at mason.gmu.edu/~jgentle/csi771/
We will cover most of Parts I, III, and IV.
Software: The main computational software is R.
R is open source and is free. It is installed on some GMU computers, but there are various binary executables available at the main R website, and it is best to load it on your own computer.
A good way to learn R is just to use it for progressively more complicated problems. While there are many books on R, the various pdf manuals that come with the installation (use "Help" on the GUI) should be sufficient.
Group work and discussion outside of class is encouraged, but of course explicit copying of homework solutions should not be done.
For in-class exams, one sheet of notes will be allowed. The preparation of that sheet is one of the most important learning activities.
Brief introduction to R.
Random number generation in R.
Saving graphics files in R.
Assignments: Read Appendix A (page 643); work problem 1.18 (page 77) to
turn in (in hardcopy on September 10).
The role of optimization in statistical estimation: minimization of residuals; maximization of likelihood; etc.
The functional approach to statistical estimation.
Assignments: Read Chapter 1; work problems 1.15, 1.16, and 1.21 to
turn in (in hardcopy on September 17).
Matrices in R.
Geometry in R.
Assignments: Read Chapter 9; work problems 1.5, 5.1, 9.1, 9.2, and 9.13 to
turn in (in hardcopy on September 24).
Assignments: Read Chapter 10; work problems 10.1, 10.3, and 10.4 to
turn in (in hardcopy on October 1).
Discuss semester project (Exercises A.2 and A.3)
Monte Carlo methods of inference
Assignment: Exercise A.2, with just 2 articles, and can be
some other statistical journal.
Methods of transforming uniform random variables to random variables from other distributions.
Data partitioning; jackknife methods.
Assignment: Work problems 7.2, 11.3, 11.7, 12.1, and 12.6 to
turn in (in hardcopy on October 29).
Assignment: Work problems 13.1, 13.2, and 13.8 to
turn in (in hardcopy on November 5).
Assignment: Work problems 14.1, 14.2, and 15.1 to
turn in (in hardcopy on November 12).
Assignment: Work problems 15.2 a) and b), 15.11, 15.13, and 16.1 to
turn in (in hardcopy on November 19).
Assignment: Work problems 16.5 and 16.7 a) to
turn in (in hardcopy on December 3) and work problem 16.8 not to turn in.
Assignment: Work problem 16.6 (not to turn in).