Computation of Spatial Gini Coefficients

Sucharita Ghosh

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 22, 4709-4720

Abstract: We address estimation of Gini coefficients for a spatial random field y(s) defined on a lattice, which is assumed to be subordinated to a latent Gaussian process Z(s) via a function G. The marginal distributions of y(s) may have arbitrary shapes and they are location dependent. For s∈N+2,${\bf s} \in \mathbb {N}^2_{+},$ we nonparametrically estimate the mean surface Ey(s)$\mathbb {E}\left\lbrace y({\bf s})\right\rbrace$ and the marginal distribution function Py(s)≤v,v∈R$P\left\lbrace y({\bf s}) \le v\right\rbrace, \ v \in \mathbb {R}$ which are used to estimate the Gini coefficients. Asymptotic results under short-memory and long-memory auto-correlations in Z are derived. An excerpt from a global total column ozone data set is used for illustration.

Date: 2015
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DOI: 10.1080/03610926.2013.823211

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