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Advances in Info-MetricsInformation and Information Processing across Disciplines$
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Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah

Print publication date: 2020

Print ISBN-13: 9780190636685

Published to Oxford Scholarship Online: December 2020

DOI: 10.1093/oso/9780190636685.001.0001

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Generalized Empirical Likelihood-Based Kernel Estimation of Spatially Similar Densities

Generalized Empirical Likelihood-Based Kernel Estimation of Spatially Similar Densities

Chapter:
(p.385) 14 Generalized Empirical Likelihood-Based Kernel Estimation of Spatially Similar Densities
Source:
Advances in Info-Metrics
Author(s):

Kuangyu Wen

Ximing Wu

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190636685.003.0014

This study concerns the estimation of spatially similar densities, each with a small number of observations. To achieve flexibility and improved efficiency, we propose kernel-based estimators that are refined by generalized empirical likelihood probability weights associated with spatial moment conditions. We construct spatial moments based on spline basis functions that facilitate desirable local customization. Monte Carlo simulations demonstrate the good performance of the proposed method. To illustruate its usefulness, we apply this method to the estimation of crop yield distributions that are known to be spatically similar.

Keywords:   kernel density estimation, generalized empirical likelihood, spatial similarity, information pooling, crop yield distribution

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