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  • Comparison of three-dimensional profiles over time

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    Accepted Manuscript (AM)
    Author(s)
    Donald, Margaret R
    Strickland, Chris
    Alston, Clair L
    Young, Rick
    Mengersen, Kerrie L
    Griffith University Author(s)
    Alston-Knox, Clair L.
    Year published
    2012
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    Abstract
    In this paper, we describe an analysis for data collected on a three-dimensional spatial lattice with treatments applied at the horizontal lattice points. Spatial correlation is accounted for using a conditional autoregressive model. Observations are defined as neighbours only if they are at the same depth. This allows the corresponding variance components to vary by depth. We use the Markov chain Monte Carlo method with block updating, together with Krylov subspace methods, for efficient estimation of the model. The method is applicable to both regular and irregular horizontal lattices and hence to data collected at any set ...
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    In this paper, we describe an analysis for data collected on a three-dimensional spatial lattice with treatments applied at the horizontal lattice points. Spatial correlation is accounted for using a conditional autoregressive model. Observations are defined as neighbours only if they are at the same depth. This allows the corresponding variance components to vary by depth. We use the Markov chain Monte Carlo method with block updating, together with Krylov subspace methods, for efficient estimation of the model. The method is applicable to both regular and irregular horizontal lattices and hence to data collected at any set of horizontal sites for a set of depths or heights, for example, water column or soil profile data. The model for the three-dimensional data is applied to agricultural trial data for five separate days taken roughly six months apart in order to determine possible relationships over time. The purpose of the trial is to determine a form of cropping that leads to less moist soils in the root zone and beyond. We estimate moisture for each date, depth and treatment accounting for spatial correlation and determine relationships of these and other parameters over time.
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    Journal Title
    Journal of Applied Statistics
    Volume
    39
    Issue
    7
    DOI
    https://doi.org/10.1080/02664763.2012.654771
    Copyright Statement
    © 2012 Taylor & Francis (Routledge). This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 31 Jan 2012, available online: https://www.tandfonline.com/doi/10.1080/02664763.2012.654771
    Subject
    Statistics
    Statistics not elsewhere classified
    Econometrics
    Publication URI
    http://hdl.handle.net/10072/173028
    Collection
    • Journal articles

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