|General Information||-||Schedule of Topics||-||Assignments||-||Textbook Errata|
|Time and Place||Tuesdays 2:30-3:20 in 110 Thomas|
Tuesdays 3:35-4:25 in 120 Thomas
Thursdays 2:30-3:20 in 110 Thomas
Because of the difficulty of getting a room for this class, we have this rather odd schedule. We'll take a short break on Tuesdays to switch rooms, stretch, chat, etc. 120 Thomas is right across the hall from 110 Thomas.
Office hours: Mondays and Fridays 3:30-4:30 (or by other arrangement, of course)
|Purpose||This course will introduce students to some of the important statistical ideas of large-sample theory without requiring any mathematics beyond calculus and linear algebra. In particular, no measure theory is required. However, a basic understanding of statistics at the level of Statistics 513--514 will be assumed.|
|Intended Audience||This course is required along with 551 for all second-year PhD students. Topics from these two courses and basic math stat will comprise the qualifying exam to be given in January after the course concludes. Since 597D is a new course, I expect that there may also be some advanced PhD students this fall. If you think you might be interested in taking it but you're not sure, please don't hesitate to come and talk to me.|
|Required Textbook||E. L. Lehmann, Elements of Large-Sample Theory
I like this book, especially for the level of this class. It is statistically rigorous without being overly mathematical and it contains many enlightening examples and exercises. Because it is a very new book, it appears to contain the requisite number of minor errors. Thus, as a lighthearted diversion, I've decided to make it a class project to come up with as complete a list as possible of mistakes. I'll send them to Lehmann at the end of the semester in case he's planning to update the book for another printing, and I may offer some small prize (a couple of exam points, perhaps) to the person who finds the most.
|Optional Textbook||T. S. Ferguson, A Course in Large Sample Theory
(Chapman and Hall, 1996)
This is a great book and I recommend it highly if you are interested in this subject. First of all, it's paperback so it's not as expensive as most statistics textbooks (it's roughly $50). Second, it has the very unusual yet enormously helpful feature that the exercises are all fully worked in the appendix (the best way to learn this stuff is to work problems, and with the solutions available to guide you when you get stuck, this book is ideal for self-study). Third, it is very concisely written, managing to pack a lot more information into the average page than the Lehmann book (partly this is because it is written almost entirely in the multivariate setting, so there is no separate treatment of the multivariate case). Fourth, it is divided into small, self-contained chunks, making it possible to sample different topics in almost any order you wish. You may wonder why, if it's such a great book, I don't use it as the main textbook for this course. The reason is that its mathematics is a bit more advanced than Lehmann's, and the whole point of this course is to present as much statistics as possible without relying on too deep a mathematical background.
|Computing||Numerical work will play a large role in the homework assignments. The software I'd recommend using is Splus, although I won't require any particular package or language. You can probably get by with Minitab if you're very comfortable with it, and packages such as Matlab or Mathematica or languages such as C or Fortran should be okay as well--however, before deciding to use one of these last 4, be sure you can obtain functions like the standard normal cdf and inverse cdf as well as random deviates from not just the uniform but all the common distributions as well. If you're not currently familiar with Splus, I strongly encourage you to learn a bit about it and if possible play around with it before the start of the class on August 22. A concise Guide to Splus by Brian Ripley is available online in postscript form, as is a more complete set of Splus Notes written by Bill Venables and Dave Smith. Finally, some documentation on Splus is available on MathSoft's website. I recommend starting with the Ripley guide, particularly sections 4, 6, 8, 11, and 12.|
|Grading||There will be two midterms (15% each), a comprehensive final exam (20%), and weekly homework (50%). The exams will probably be closed-book, though we'll discuss the details in class as the dates for them draw nearer. Probably, I'll make such decisions after I get input on students' preferences, since for many students the result of the qualifying exam is ultimately more important than the grade in this class and I'd like to structure the grading to provide the best possible preparation for the qualifier.|