Spatial spline regression models

We describe a model for the analysis of data distributed over irregularly shaped spatial domains with complex boundaries, strong concavities and interior holes. Adopting an approach that is typical of functional data analysis, we propose a spatial spline regression model that is computationally effi...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2013-09, Vol.75 (4), p.681-703
Main Authors: Sangalli, Laura M., Ramsay, James O., Ramsay, Timothy O.
Format: Article
Language:English
Subjects:
Boundaries
Boundary conditions
Computational efficiency
Concavity
Covariance
Data analysis
Data processing
Data smoothing
Estimation
Estimation methods
Estimators
Finite elements
Functional data analysis
Information
Mathematical analysis
Mathematical functions
Mathematical models
Modeling
Penalized smoothing
Population density
Regression
Regression analysis
Semiparametric model
Soaps
Spatial analysis
Spatial data analysis
Splines
Statistical analysis
Statistics
Studies
Triangulation
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Summary:We describe a model for the analysis of data distributed over irregularly shaped spatial domains with complex boundaries, strong concavities and interior holes. Adopting an approach that is typical of functional data analysis, we propose a spatial spline regression model that is computationally efficient, allows for spatially distributed covariate information and can impose various conditions over the boundaries of the domain. Accurate surface estimation is achieved by the use of piecewise linear and quadratic finite elements.
ISSN:1369-7412
1467-9868
DOI:10.1111/rssb.12009