Selected article for: "linear regression and statistical significance"

Author: Emmendorfer, Leonardo Ramos; Dimuro, Graçaliz Pereira
Title: A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression
  • Cord-id: ugokh2uq
  • Document date: 2020_6_15
  • ID: ugokh2uq
    Snippet: Inverse Distance Weighting (IDW) is a widely adopted interpolation algorithm. This work presents a novel formulation for IDW which is derived from a weighted linear regression. The novel method is evaluated over study cases related to elevation data, climate and also on synthetic data. Relevant aspects of IDW are preserved while the novel algorithm achieves better results with statistical significance. Artifacts are alleviated in interpolated surfaces generated by the novel approach when compare
    Document: Inverse Distance Weighting (IDW) is a widely adopted interpolation algorithm. This work presents a novel formulation for IDW which is derived from a weighted linear regression. The novel method is evaluated over study cases related to elevation data, climate and also on synthetic data. Relevant aspects of IDW are preserved while the novel algorithm achieves better results with statistical significance. Artifacts are alleviated in interpolated surfaces generated by the novel approach when compared to the respective surfaces from IDW.

    Search related documents: