On spline-based approaches to spatial linear regression for geostatistical data

Guilherme Ludwig, Jun Zhu, Perla Reyes, Chun Shu Chen, Shawn P. Conley

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

For spatial linear regression, the traditional approach to capture spatial dependence is to use a parametric linear mixed-effects model. Spline surfaces can be used as an alternative to capture spatial variability, giving rise to a semiparametric method that does not require the specification of a parametric covariance structure. The spline component in such a semiparametric method, however, impacts the estimation of the regression coefficients. In this paper, we investigate such an impact in spatial linear regression with spline-based spatial effects. Statistical properties of the regression coefficient estimators are established under the model assumptions of the traditional spatial linear regression. Further, we examine the empirical properties of the regression coefficient estimators under spatial confounding via a simulation study. A data example in precision agriculture research regarding soybean yield in relation to field conditions is presented for illustration.

Original languageEnglish
Pages (from-to)175-202
Number of pages28
JournalEnvironmental and Ecological Statistics
Volume27
Issue number2
DOIs
StatePublished - 1 Jun 2020

Keywords

  • Linear mixed-effects models
  • Precision agriculture
  • Semiparametric methods
  • Spatial confounding
  • Spatial statistics

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