P-spline smoothing for spatial data collected worldwide

@article{Greco2018PsplineSF,
  title={P-spline smoothing for spatial data collected worldwide},
  author={Fedele Pasquale Greco and Massimo Ventrucci and Elisa Castelli},
  journal={Spatial Statistics},
  year={2018}
}

Figures and Tables from this paper

Recent developments in complex and spatially correlated functional data
TLDR
The functional data approach offers a new paradigm of data analysis, where the continuous processes or random fields are considered as a single entity, and is considered to be very valuable in the context of big data.
General P-Splines for Non-Uniform B-Splines
TLDR
Though both penalties seem equally powerful in wiggliness control for their mathematical association and statistical similarity, simulation studies show that general P-spline either outperforms O- Spline in terms of mean squared error, or performs equally well, making it a superior replacement of O- spline.
Automatic Search Interval for Smoothing Parameter in Penalized Splines
TLDR
Novel algorithms are developed to automatically find a smoothing parameter range where the penalized least squares problem is numerically solvable and may be embedded in other advanced statistical modeling methods that rely on penalized splines.
Enthalpy thematic map interpolated with spline method for management of broiler chicken production
ABSTRACT Owing to the exponential growth of the human population and problems related to food supply, research focused on finding the most suitable approach to manage and geographically explore the
Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic
TLDR
A positive association between neighborhood social disadvantage and infections is found and differences in capacity to socially isolate is a credible pathway between disadvantage and COVID-19 disparities, supported by analysis of Census Bureau and NYC open data and SARS-CoV-2 testing data.
Thermal comfort of beef cattle in the state of Mato Grosso do Sul, Brazil
ABSTRACT Evaluation of the comfort and animal welfare parameters enables determining the best environmental conditions for livestock creation. The present study was aimed to determine the thermal
Lattice-based methods for regression and density estimation on complicated multidimensional regions
This paper illustrates the use of diffusion kernels to estimate smooth density and regression functions defined on highly complex domains. We generalize the two-dimensional lattice-based estimators

References

SHOWING 1-10 OF 50 REFERENCES
Gaussian predictive process models for large spatial data sets.
TLDR
This work achieves the flexibility to accommodate non‐stationary, non‐Gaussian, possibly multivariate, possibly spatiotemporal processes in the context of large data sets in the form of a computational template encompassing these diverse settings.
Constructing valid spatial processes on the sphere using kernel convolutions
Remotely sensed data products are now routinely used to study various aspects of the Earth's atmosphere. These remote sensing datasets are typically very high dimensional, have near global coverage
High-Dimensional Bayesian Geostatistics.
TLDR
Two approaches can be described as model-based solutions for big spatiotemporal datasets that ensure that the algorithmic complexity has ~ n floating point operations (flops), where n the number of spatial locations (per iteration).
A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets
TLDR
A multiresolution model to predict two-dimensional spatial fields based on irregularly spaced observations that gives a good approximation to standard covariance functions such as the Matérn and also has flexibility to fit more complicated shapes.
Covariance Tapering for Interpolation of Large Spatial Datasets
TLDR
It is shown that tapering the correct covariance matrix with an appropriate compactly supported positive definite function reduces the computational burden significantly and still leads to an asymptotically optimal mean squared error.
Bayesian P-Splines
P-splines are an attractive approach for modeling nonlinear smooth effects of covariates within the additive and varying coefficient models framework. In this article, we first develop a Bayesian
Smooth-CAR mixed models for spatial count data
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
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
TLDR
A class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets are developed and it is established that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices.
An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
TLDR
It is shown that, using an approximate stochastic weak solution to (linear) stochastically partial differential equations, some Gaussian fields in the Matérn class can provide an explicit link, for any triangulation of , between GFs and GMRFs, formulated as a basis function representation.
...
...