TY - JOUR
T1 - Birnbaum-Saunders spatial regression models
T2 - Diagnostics and application to chemical data
AU - Garcia-Papani, Fabiana
AU - Leiva, Víctor
AU - Uribe-Opazo, Miguel A.
AU - Aykroyd, Robert G.
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/6/15
Y1 - 2018/6/15
N2 - Geostatistical modelling is widely used to describe data with spatial dependence structure. Such modelling often assumes a Gaussian distribution, an assumption which is frequently violated due to the asymmetric nature of variables in diverse applications. The Birnbaum-Saunders distribution is asymmetrical and has several appealing properties, including theoretical arguments for describing chemical data. This work examines a Birnbaum-Saunders spatial regression model and derives global and local diagnostic methods to assess the influence of atypical observations on the maximum likelihood estimates of its parameters. Modelling and diagnostic methods are then applied to experimental data describing the spatial distribution of magnesium and calcium in the soil in the Parana state of Brazil. This application shows the importance of such a diagnostic analysis in spatial modelling with chemical data.
AB - Geostatistical modelling is widely used to describe data with spatial dependence structure. Such modelling often assumes a Gaussian distribution, an assumption which is frequently violated due to the asymmetric nature of variables in diverse applications. The Birnbaum-Saunders distribution is asymmetrical and has several appealing properties, including theoretical arguments for describing chemical data. This work examines a Birnbaum-Saunders spatial regression model and derives global and local diagnostic methods to assess the influence of atypical observations on the maximum likelihood estimates of its parameters. Modelling and diagnostic methods are then applied to experimental data describing the spatial distribution of magnesium and calcium in the soil in the Parana state of Brazil. This application shows the importance of such a diagnostic analysis in spatial modelling with chemical data.
KW - Geochemical data analysis
KW - Global and local influence
KW - Matérn model
KW - Maximum likelihood methods
KW - Non-normality
KW - R software
UR - http://www.scopus.com/inward/record.url?scp=85046659663&partnerID=8YFLogxK
U2 - 10.1016/j.chemolab.2018.03.012
DO - 10.1016/j.chemolab.2018.03.012
M3 - Article
AN - SCOPUS:85046659663
SN - 0169-7439
VL - 177
SP - 114
EP - 128
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
ER -