HyperNiche Other Applications

Reference DOI,
Arkle et al. (2012) DOI
quantitative (fire severity from remote sensing) Local linear NPMR, Gaussian weights; topographic and spatial variables as predictors
McCune (2007) PDF quantitative (potential direct incident radiation) Local linear NPMR, Gaussian weights, with slope, aspect, and latitude as predictors
McCune, S. (2010) PDF Geomorphology: probability of stream connectivity Binary response, local mean NPMR, Gaussian weights. Extensive use of response curve "slices".
Nicolaou & Constandinou (2012) DOI Neuroscience: simulated nonlinear interacting systems and physiological responses Causality estimation. From Abstract: " Autoregressive modeling is replaced by ...NPMR. NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how ... the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply CNPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). CNPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. CNPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonparametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications.