Comparison of three-dimensional profiles over time
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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|Item Type:||Journal Article|
|Divisions:||Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 Taylor and Francis|
|Copyright Statement:||This is a postprint of an article whose final and definitive form has been published in the [Journal of Applied Statistics] (C)  (copyright Taylor & Francis); [JJournal of Applied Statistics] is available online at: http://www.tandfonline.com/doi/abs/10.1080/02664763.2012.654771|
|Deposited On:||02 Feb 2012 04:55|
|Last Modified:||21 Jun 2015 18:26|
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