Garcia-Papani, F, Uribe-Opazo, MA, Leiva, V et al. (1 more author) (2017) Birnbaum-Saunders spatial modelling and diagnostics applied to agricultural engineering data. Stochastic Environmental Research and Risk Assessment, 31 (1). pp. 105-124. ISSN 1436-3240
Abstract
Applications of statistical models to describe spatial dependence in geo-referenced data are widespread across many disciplines including the environmental sciences. Most of these application assume that the data follow a Gaussian distributions. However, in many of them the normality assumption, and even a more general assumption of symmetry, are not appropriate. In non-spatial applications, where the data are uni-modal and positively skewed, the Birnbaum-Saunders distribution has excelled. This paper proposes a spatial log-linear model based in the Birnbaum-Saunders distribution. Model parameters are estimated using the maximum likelihood method. Local influence diagnostics are derived to assess the sensitivity of the estimators to perturbations in the response variable. As illustration, the proposed model and its diagnostics are used to analyse a real-world agricultural data-set, where the spatial variability of phosphorus concentration in the soil is considered- which is extremely important for agricultural management.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016, Springer. This is an author produced version of a paper published in Stochastic Environmental Research and Risk Assessment. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via https://doi.org/10.1007/s00477-015-1204-4 |
Keywords: | Asymmetric distributions; local influence; Matérn model; maximum likelihood method; Monte Carlo simulation; non-normality; R software; spatial data analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2016 13:40 |
Last Modified: | 18 Jul 2017 06:56 |
Published Version: | https://doi.org/10.1007/s00477-015-1204-4 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/s00477-015-1204-4 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:93197 |