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    Data imputation in a short-run space-time series: A Bayesian approach

    Pforte, Lars and Brunsdon, Chris and Cahalane, Conor and Charlton, Martin (2017) Data imputation in a short-run space-time series: A Bayesian approach. Environment and Planning B: Planning and Design, 45 (5). pp. 864-887. ISSN 1472-3417

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    This paper discusses a project on the completion of a database of socio-economic indicators across the European Union for the years from 1990 onward at various spatial scales. Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database. A Markov Chain Monte Carlo approach was opted for. We describe the Markov Chain Monte Carlo method in detail. Furthermore, we explain how we achieved spatial coherence between different time series and their observed and estimated data points.

    Item Type: Article
    Keywords: Time series; Markov Chain Monte Carlo; data imputation;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Faculty of Social Sciences > Geography
    Faculty of Social Sciences > Research Institutes > Maynooth University Social Sciences Institute, MUSSI
    Item ID: 11351
    Identification Number:
    Depositing User: Conor Cahalane
    Date Deposited: 16 Oct 2019 16:21
    Journal or Publication Title: Environment and Planning B: Planning and Design
    Publisher: SAGE Publications
    Refereed: Yes

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