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Type: Journal article
Title: Estimation in semiparametric spatial regression
Author: Gao, J.
Lu, Z.
Tjostheim, D.
Citation: Annals of Statistics, 2006; 34(3):1395-1435
Publisher: Inst Mathematical Statistics
Issue Date: 2006
ISSN: 0090-5364
Statement of
Jiti Gao, Zudi Lu and Dag Tjøstheim
Abstract: Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvious reason for this is the curse of dimensionality. For spatial data on a grid evaluating the conditional mean given its closest neighbors requires a four-dimensional nonparametric regression. In this paper a semiparametric spatial regression approach is proposed to avoid this problem. An estimation procedure based on combining the so-called marginal integration technique with local linear kernel estimation is developed in the semiparametric spatial regression setting. Asymptotic distributions are established under some mild conditions. The same convergence rates as in the one-dimensional regression case are established. An application of the methodology to the classical Mercer and Hall wheat data set is given and indicates that one directional component appears to be nonlinear, which has gone unnoticed in earlier analyses
Description: Also published in: arXiv:math/0608053v1
Rights: © Institute of Mathematical Statistics, 2006. Submitted to Cornell University’s online archive in 2006 by Jiti Gao. Post-print sourced from
RMID: 0020081878
DOI: 10.1214/009053606000000317
Published version:
Appears in Collections:Economics publications

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