|When:||Tuesday, Thursday 10:30 - 11:45 (might be moved)|
|Where:||24 Hillhouse, basement classroom|
|Problem session:||to be arranged if needed|
|Other:||courses taught by DP in previous years|
|Short description:||A careful study of some standard asymptotic techniques in statistics and econometrics, and their modern refinements. Topics selected from classical likelihood theory and M-estimation; empirical process methods; concentration inequalities; semiparametric models; local asymptotic normality; concepts of efficiency. Prerequisites: knowledge of probability at the level of STAT 600b.|
|Intended audience:||Students (both graduate and undergraduate) who have had some exposure to measure theoretic probability and who need to understand some asymptotic theory at the reserch level.|
For many years I have been working on a book [Asymptopia: current
table of contents] on modern
asymptotic theory. Recently I decided it is getting too big; I
decided to split a big manuscript into two or three smaller books.
Some of the material will be covered in Stat 618.
Chapters from Asymptopia will be posted in the Handouts directory after they receive another edit.
|Grading:||The grading method will depend on the the audience for the course. I am leaning towards a variation on the method I used for Stat 603 last year, but with more traditional homework sheets.|
|Topics:|| Tentative list. The actual material covered will depend, in part, on the backgrounds of students in the class.