This thesis comprises two parts: (I) estimating transmission rates on a network; and (II) poststratification as a unified framework for the analysis of sample surveys.
In Part I, we solve the problem of estimating person-to-person transmission rates of a contagious process on a network. We conduct a simulation study to demonstrate that despite the infection times of the individuals being missing data, we are able to estimate the transmission rates. We apply our method to a data set where the individuals in the network are babies in a neo-natal intensive care unit and the disease of interest is MRSA (Methicillin-resistant Staphycocculus Aurerus).
In Part II, we pose the problem of developing a Bayesian unified framework for the analysis of sample survey data that incorporates uncertainty about design and census numbers, and we formulate such a framework. Using this framework, it is now possible to estimate population means and population regression functions using poststratification even when census numbers are unavailable. Further, it is now possible to incorporate survey weights into a model-based analysis. We illustrate our methods on a data set on attitudes towards gay rights. We demonstrate that we can estimate the nonlinear pattern of support for gay unions, as a function of age of respondent, properly including the information in survey weights.
|Advisor:||Gelman, Andrew, Dolgoarshinnykh, Regina|
|School Location:||United States -- New York|
|Source:||DAI-B 71/03, Dissertation Abstracts International|
|Keywords:||Population inference, Poststratification, Transmission rates|
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