A Spatial Logistic Regression Model Based on a Valid Skew-Gaussian Latent Field

Vahid Tadayon () and Mohammad Mehdi Saber ()
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Vahid Tadayon: Higher Education Center of Eghlid
Mohammad Mehdi Saber: Higher Education Center of Eghlid

Journal of Agricultural, Biological and Environmental Statistics, 2023, vol. 28, issue 1, No 5, 59-73

Abstract: Abstract Logistic regression is commonly used to estimate the association of one (or more) independent variable(s) with a binary- dependent outcome. In many applications latent sources are both spatially dependent and non-Gaussian; thus, it is desirable to exploit both properties jointly. Spatial logistic regression is a well-established technique of including spatial dependence in logistic regression models. In this paper, we develop a spatial logistic regression model based on a valid skew-Gaussian random field. For parameter estimation, we use a Monte Carlo extension of the EM algorithm along with an approximation based on the standard logistic function. A simulation study is applied in order to determine the performance of the proposed model and also to compare the results with a recently introduced model with established efficiency. The identifiability of the parameters is investigated as well. As an illustrative purpose, an application to the Meuse heavy metals dataset is presented. Supplementary materials accompanying this paper appear online.

Keywords: Binary spatial data; MCEM algorithm; Spatial modeling; Non-Gaussian random field (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13253-022-00512-3

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