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 language | English |
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Pages (from-to) | 175-202 |
Number of pages | 28 |
Journal | Environmental and Ecological Statistics |
Volume | 27 |
Issue number | 2 |
DOIs | |
State | Published - 1 Jun 2020 |
Keywords
- Linear mixed-effects models
- Precision agriculture
- Semiparametric methods
- Spatial confounding
- Spatial statistics