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Non-Linear Choice Modelling with Gaussian Processes: A Case Study of Neighbourhood Choice in Stockholm
University of Leeds - School of Mathematics.
School of Politics and International Studies, University of Leeds.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Institute for Housing and Urban Research.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
2016 (English)Report (Other academic)
Abstract [en]

We present a non-parametric extension of the conditional logit model, using Gaussian pro- cess priors. The conditional logit model is used in quantitative social science for inferring interaction effects between personal features and choice characteristics from observations of individual multinomial decisions, such as where to live, which car to buy or which school to choose. The classic, parametric model presupposes a latent utility function that is a linear combination of choice characteristics and their interactions with personal features. This imposes strong and unrealistic constraints on the form of individuals’ preferences. Extensions using non-linear basis functions derived from the original features can ameliorate this problem but at the cost of high model complexity and increased reliance on the user in model specification. In this paper we develop a novel, non-parametric conditional logit model based on Gaussian process logit models. We demonstrate its application on housing choice data from over 50,000 moving households from the Stockholm area over a two year period to reveal complex homophilic patterns in income, ethnicity and parental status.

Place, publisher, year, edition, pages
Elsevier, 2016. , 36 p.
Series
SSRN report
National Category
Human Geography Mathematics
Identifiers
URN: urn:nbn:se:uu:diva-311123OAI: oai:DiVA.org:uu-311123DiVA: diva2:1058709
Available from: 2016-12-21 Created: 2016-12-21 Last updated: 2017-02-20

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https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2869215

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Hedman, LinaSumpter, David J.T.
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