# Semiparametric regression models for spatial prediction and uncertainty quantification of soil attributes

@article{Merrill2016SemiparametricRM, title={Semiparametric regression models for spatial prediction and uncertainty quantification of soil attributes}, author={Hunter R. Merrill and Sabine Grunwald and Nikolay Bliznyuk}, journal={Stochastic Environmental Research and Risk Assessment}, year={2016}, volume={31}, pages={2691-2703} }

In many studies, the distribution of soil attributes depends on both spatial location and environmental factors, and prediction and process identification are performed using existing methods such as kriging. However, it is often too restrictive to model soil attributes as dependent on a known, parametric function of environmental factors, which kriging typically assumes. This paper investigates a semiparametric approach for identifying and modeling the nonlinear relationships of spatially…

## 5 Citations

### Bayesian measurement error correction in structured additive distributional regression with an application to the analysis of sensor data on soil–plant variability

- MathematicsStochastic Environmental Research and Risk Assessment
- 2019

The flexibility of the Bayesian approach to account for covariates with measurement error is combined with semiparametric regression models. We consider a class of continuous, discrete and mixed…

### Spatio-temporal additive regression model selection for urban water demand

- Computer ScienceStochastic Environmental Research and Risk Assessment
- 2019

A method for Bayesian variable selection in partially linear additive regression models, particularly suited for high-dimensional spatio-temporally dependent data, is developed.

### Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A Comparative study

- Environmental Science, EconomicsEnviron. Model. Softw.
- 2018

### Reshaping How We Think about Soil Security

- Soil Systems
- 2022

The soil security framework has been conceptualized and views soil as a resource that needs to be secured to avoid or minimize adverse environmental/anthropogenic impacts and undesirable consequences…

### Complete consistency for the weighted least squares estimators in semiparametric regression models

- MathematicsCommunications in Statistics - Theory and Methods
- 2022

## References

SHOWING 1-10 OF 38 REFERENCES

### Holistic environmental soil-landscape modeling of soil organic carbon

- Computer Science, EngineeringEnviron. Model. Softw.
- 2014

### Quantification of model uncertainty in environmental modeling

- Environmental Science
- 2010

The last few decades have seen considerable progress in the quantification of environmental model uncertainty. Initially the emphasis has been on uncertainty in model parameters. A more recent trend…

### Uncertainty assessment of heavy metal soil contamination mapping using spatiotemporal sequential indicator simulation with multi-temporal sampling points

- Environmental ScienceEnvironmental Monitoring and Assessment
- 2015

STSIS performs better than both STK and SIS in uncertainty assessment of heavy metal contamination of soils, and the comparison of the STSIS technique vs. SIS and STK techniques showed that STSis performs better.

### Bayesian Calibration and Uncertainty Analysis for Computationally Expensive Models Using Optimization and Radial Basis Function Approximation

- Computer Science
- 2008

This work presents a Bayesian approach to model calibration when evaluation of the model is computationally expensive, and approximate the logarithm of the posterior density using radial basis functions and uses the resulting cheap-to-evaluate surface in MCMC.

### Quantification of the predictive uncertainty of artificial neural network based river flow forecast models

- EngineeringStochastic Environmental Research and Risk Assessment
- 2012

The meaningful quantification of uncertainty in hydrological model outputs is a challenging task since complete knowledge about the hydrologic system is still lacking. Owing to the nonlinearity and…

### Assessing uncertainty in soil organic carbon modeling across a highly heterogeneous landscape

- Environmental Science
- 2015

### NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES.

- Computer ScienceThe annals of applied statistics
- 2014

This work considers a Bayesian hierarchical framework in which a joint model consists of a set of submodels, one for each data source, and a model for the latent process that serves to relate the submodels to one another, and proposes an efficient MCMC scheme that takes advantage of these approximations.

### Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models

- Computer Science
- 2004

It is proposed that GAM's with a ridge penalty provide a practical solution in such circumstances, and a multiple smoothing parameter selection method suitable for use in the presence of such a penalty is developed.

### Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

- Mathematics
- 2011

Summary. Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring restricted maximum likelihood (REML) to generalized cross‐validation (GCV) for smoothing parameter…

### A Matérn model of the spatial covariance structure of point rain rates

- Environmental ScienceStochastic Environmental Research and Risk Assessment
- 2014

It is challenging to model a precipitation field due to its intermittent and highly scale-dependent nature. Many models of point rain rates or areal rainfall observations have been proposed and…