[Forum SIS] PhD Short Course - Prof. Ray Chambers - An Introduction to Model-Based Sampling With Applications

[Riccardo Borgoni]

PhD program in statistics – University of Milan Bicocca

An Introduction to Model-Based Sampling With Applications
Prof. R. Chambers
University of Wollongong

Summary
This 2 day course is based on the text with the 
same name by Chambers and Clark that will be 
published by Oxford University Press later this 
year. After introducing some basic concepts in 
survey sampling, the first day of the course will 
focus on sample design and estimation under three 
basic population data models that are in common 
use, the stratified population model, the 
regression population model and the clustered 
population model. All three are examples of the 
general linear population model, and optimal 
estimation methods for this model will also be 
discussed. The second day of the course will then 
focus on robust model-based methods for sample 
survey inference, and in particular on model 
misspecification robust methods as well as 
outlier robust methods. If time permits the 
course will finish with an overview of the use of 
model-based methods in small area estimation, 
finite population distribution function 
estimation and the use of transformations to linearity in survey inference.
The course is aimed at final year undergraduate 
and post-graduate students with a good grounding 
in statistics, including a course in the theory 
of linear regression. Matrix notation will be 
used, particularly in day 2. The course should 
also be useful for applied survey statisticians 
who are looking for an introduction to the use of 
models in survey design and estimation and who 
have some familiarity with surveys and statistics.
The course will be held at the Department of 
Statistics, University of Milan-Bicocca building 
U7, 2nd floor, room 2019, on 23rd and 24th June 2011.

Each day the timing of the course is as follows:
Lecture 1               10:00 - 11:15
Break                     11:15 - 11:30
Lecture 2               11:30 - 12:45
Break                      12:45 - 14:00
Lecture 3                14:00 - 15:15
Break                      15:15 - 15:30
Lecture 4                15:30 - 16:45
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