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 or Dissertation (PhD)
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory
Official Date:	2011
Dates:	Date Event 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) ; Engineering and Physical Sciences Research Council
Description:	Published articles based on this thesis: Goudie, R. J. B., & Mukherjee, S. (2016). A Gibbs Sampler for Learning DAGs. Journal of Machine Learning Research, 17(30), 1–39. Goudie, R. J. B., Mukherjee, S., de Neve, J.-E., de Neve, J.-E., Oswald, A. J., & Wu, S. (2014). Happiness as a driver of risk-avoiding behaviour: Theory and an empirical study of seatbelt wearing and automobile accidents. Economica, 81(324), 674–697. Goudie, R. J. B., Mukherjee, S., and Griffiths, F. (2011). Exploratory network analysis of large social science questionnaires. In: Proceedings of the Eighth UAI Bayesian Modeling Applications Workshop (UAI-AW 2011). Ed. by A. Nicholson.
Extent:	xix, 189 leaves ; charts
Language:	eng
Related URLs:	http://jmlr.org/papers/v17/14-486.html http://doi.org/10.1111/ecca.12094 http://ceur-ws.org/Vol-818/paper5.pdf

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