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Efficient estimation of nonparametric spatial models with general correlation structures
Author(s) -
Wang Hongxia,
Wu Yuehua,
Chan Elton
Publication year - 2017
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12192
Subject(s) - estimator , nonparametric statistics , mathematics , spatial correlation , correlation , asymptotic distribution , estimation , normality , statistics , econometrics , geometry , management , economics
Summary Spatially correlated data appear in many environmental studies, and consequently there is an increasing demand for estimation methods that take account of spatial correlation and thereby improve the accuracy of estimation. In this paper we propose an iterative nonparametric procedure for modelling spatial data with general correlation structures. The asymptotic normality of the proposed estimators is established under mild conditions. We demonstrate, using both simulation and case studies, that the proposed estimators are more efficient than the traditional locally linear methods which fail to account for spatial correlation.