Gradient-based Stochastic Local Interaction (SLI) Model

SLI_GRADIENT_MLE_2020 IS A SOFTWARE BUNDLE THAT IMPLEMENTS THE
GRADIENT-BASED STOCHASTIC LOCAL INTERACTION SPATIAL MODEL in D dimensions.

EXAMPLES: Examples for SLI parameter estimation, interpolation, and
  mapping  are given in the script Main_examples.m

The three main functions for SLI estimation, interpolation and mapping
  are those having names that begin with "Main":

  1. Main_sli_mle: It should be run before the other scripts.
  It estimates the SLI parameters for a specified dataset.
  2. Main_loocv_sli: This function can be run after Main_sli_mle.m if it
  is desired to obtain Leave-One-Out Cross validation estimates.
  3. Main_sli_map: This function is executed in order to interpolate a
  give dataset and generated predictions at the nodes of a map grid. This
  works only for two-dimensional data z(x,y)

  4. INTERPOLATION AT SET OF P POINTS
  Note that if the target is to predict the unknown values at a number of
  sites (not necessarily on a map) one can simply execute the command:

  [ Zphat, svar ] = Predict_SLI(dd, locs, zs, plocs, kernel, K, modelsli, Param_sli );

  where:
  dd:         Space dimension
  locs:       Data locations (array N x dd)
  zs:         Data values (array N x 1)
  plocs:      Prediction Locations (array P x dd)
  kernel:     String specifying kernel function (help kernel_s for
  different types)
  K:          Neighbor order (K=3 should be adequate)
  modelsli:   SLI model structure (you need to first run Main_sli_mle.m
  for the same dataset)
  Param_sli:  SLI parameter vector (you need to first run Main_sli_mle.m
  for the same dataset)
 

Copyright (C) 2020  Dionisis Hristopulos, Andreas Pavlides, Vasiliki Agou
 email: dchristopoulos@tuc.gr

 This bundle is free software; you can redistribute it and/or modify it
 under the terms of the GNU General Public License as published by the
 Free Software Foundation (version 2 of the License or  later version).

 This program is distributed WITHOUT ANY WARRANTY.
 See the GNU General Public License for more details.
 

If you use this software, please cite the publication:

Hristopulos, D.T., Pavlides, A., Agou, V.D. et al. Stochastic Local Interaction Model: An Alternative to Kriging for Massive Datasets. Math Geosci 53, 1907–1949 (2021). https://doi.org/10.1007/s11004-021-09957-7