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

Fabiana Garcia-Papani, Miguel Angel Uribe-Opazo, Victor Leiva, Robert G. Aykroyd

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

45 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)105-124
Número de páginas20
PublicaciónStochastic Environmental Research and Risk Assessment
Volumen31
N.º1
DOI
EstadoPublicada - 1 ene 2017
Publicado de forma externa

Huella

Profundice en los temas de investigación de 'Birnbaum–Saunders spatial modelling and diagnostics applied to agricultural engineering data'. En conjunto forman una huella única.

Citar esto