NDLI: Birnbaum–Saunders spatial modelling and diagnostics applied to agricultural engineering data

Content Provider	Springer Nature Link
Author	Garcia Papani, Fabiana Uribe Opazo, Miguel Angel Leiva, Victor Aykroyd, Robert G.
Copyright Year	2016
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 applications assume that the data follow a Gaussian distribution. 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 (BS) distribution has excelled. This paper proposes a spatial log-linear model based on the BS 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.
Starting Page	105
Ending Page	124
Page Count	20
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	31
Issue Number	1
e-ISSN	14363259
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-02-02
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Asymmetric distributions Local influence Matérn model Maximum likelihood methods Monte Carlo simulation Non-normality R software Spatial data analysis Math. Application in Environmental Science Earth Sciences Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Computational Intelligence Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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