Garcia-Papani, F, Leiva, V, Uribe-Opazo, MA et al. (1 more author) (2018) Birnbaum-Saunders spatial regression models: Diagnostics and application to chemical data. Chemometrics and Intelligent Laboratory Systems, 177. pp. 114-128. ISSN 0169-7439
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier B.V. This is an author produced version of a paper published in Chemometrics and Intelligent Laboratory Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Geochemical data analysis; Global and local influence; Matérn model; Maximum likelihood methods; Non-normality; R software |
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: | 23 Mar 2018 16:29 |
Last Modified: | 29 Mar 2019 01:43 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.chemolab.2018.03.012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128914 |