Understanding the stochastic partial differential equation approach to smoothing
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| Authors | David L. Miller, Richard Glennie, Andrew E. Seaton |
| Journal/Conference Name | Journal of Agricultural, Biological, and Environmental Statistics |
| Paper Category | Agricultural and Biological Sciences |
| Paper Abstract | Correlation and smoothness are terms used to describe a wide variety of random quantities. In time, space, and many other domains, they both imply the same idea quantities that occur closer together are more similar than those further apart. Two popular statistical models that represent this idea are basis-penalty smoothers (Wood in Texts in statistical science, CRC Press, Boca Raton, 2017) and stochastic partial differential equations (SPDEs) (Lindgren et al. in J R Stat Soc Series B (Stat Methodol) 73(4)423–498, 2011). In this paper, we discuss how the SPDE can be interpreted as a smoothing penalty and can be fitted using the R package mgcv, allowing practitioners with existing knowledge of smoothing penalties to better understand the implementation and theory behind the SPDE approach. |
| Date of publication | 2019 |
| Code Programming Language | R |
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