Goudie, Robert J. B. (2011) Bayesian structural inference with applications in social science. PhD thesis, University of Warwick.

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Abstract

Structural inference for Bayesian networks is useful in situations where the underlying
relationship between the variables under study is not well understood. This is
often the case in social science settings in which, whilst there are numerous theories
about interdependence between factors, there is rarely a consensus view that would
form a solid base upon which inference could be performed. However, there are
now many social science datasets available with sample sizes large enough to allow
a more exploratory structural approach, and this is the approach we investigate in
this thesis.
In the first part of the thesis, we apply Bayesian model selection to address a key
question in empirical economics: why do some people take unnecessary risks with
their lives? We investigate this question in the setting of road safety, and demonstrate
that less satisfied individuals wear seatbelts less frequently.
Bayesian model selection over restricted structures is a useful tool for exploratory
analysis, but fuller structural inference is more appealing, especially when there is
a considerable quantity of data available, but scant prior information. However,
robust structural inference remains an open problem. Surprisingly, it is especially
challenging for large n problems, which are sometimes encountered in social science.
In the second part of this thesis we develop a new approach that addresses this
problem - a Gibbs sampler for structural inference, which we show gives robust
results in many settings in which existing methods do not.
In the final part of the thesis we use the sampler to investigate depression in adolescents
in the US, using data from the Add Health survey. The result stresses the
importance of adolescents not getting medical help even when they feel they should,
an aspect that has been discussed previously, but not emphasised.

Item Type:	Thesis (PhD)
Subjects:	H Social Sciences > HM Sociology Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Mathematical statistics, Social sciences -- Statistical methods
Official Date:	October 2011
Dates:	Date Event October 2011 Submitted
Institution:	University of Warwick
Theses Department:	Department of Statistics ; School of Health and Social Studies
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Mukherjee, Sach ; Griffiths, Frances
Sponsors:	Economic and Social Research Council (Great Britain) (ESRC) ; Engineering and Physical Sciences Research Council (EPSRC)
Extent:	xix, 189 leaves : charts
Language:	eng

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