Description

This course is designed for graduate students across the university who wish to acquire a deep understanding of the theory and methods in probability sampling. Sampling is one of the classical areas of statistics with potential application in almost every aspect of research in empirical sciences. Although many of the theoretical results have been known for a while, it is the last decade or so when computing developments have made it possible to put these results into practical applications and, as a consequence, a great deal of new problems based on the increasingly demanding challenges posed by empirical research have emerged. The aim of the course is to provide the student with a review of sampling theory under the Horvitz-Thompson paradigm followed by a treatment of adaptive sampling so that at the completion of the course the students are prepared to address sampling issues in a wide spectrum of situations.

Prerequisites

None

References

• A. Chaudhuri and H. Stenger. 2005. Survey sampling: theory and methods, 2nd ed. Chapman and Hall.
• W.G. Cochran. 1977. Sampling techniques, 3rd ed. Wiley.
• Z. Govindarajulu. 1999. Elements of sampling theory and methods. Prentice Hall.
• S. L. Lohr. 2009. Sampling: design and analysis. Brooks/Cole.
• M.E. Thompson. 1997. Theory of sample surveys. Chapman and Hall.
• S. K. Thompson. 2002. Sampling, 2nd ed. Wiley.
• S. K. Thompson and G. A. F. Seber. 2002. Adaptive sampling. Wiley.