Statistical Methods & Applications

, Volume 22, Issue 1, pp 97–112

Discussing the “big n problem”

  • Giovanna Jona Lasinio
  • Gianluca Mastrantonio
  • Alessio Pollice

DOI: 10.1007/s10260-012-0207-2

Cite this article as:
Jona Lasinio, G., Mastrantonio, G. & Pollice, A. Stat Methods Appl (2013) 22: 97. doi:10.1007/s10260-012-0207-2


When a large amount of spatial data is available computational and modeling challenges arise and they are often labeled as “big n problem”. In this work we present a brief review of the literature. Then we focus on two approaches, respectively based on stochastic partial differential equations and integrated nested Laplace approximation, and on the tapering of the spatial covariance matrix. The fitting and predictive abilities of using the two methods in conjunction with Kriging interpolation are compared in a simulation study.


SPDEINLATaperingLarge spatial data setsSpatial statistics

Supplementary material

10260_2012_207_MOESM1_ESM.r (6 kb)
ESM 1 (R 6 kb)

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Giovanna Jona Lasinio
    • 1
  • Gianluca Mastrantonio
    • 1
  • Alessio Pollice
    • 2
  1. 1.Department of Statistical SciencesSapienza University of RomeRomeItaly
  2. 2.Department of Economics and MathematicsUniversity of Bari “Aldo Moro”BariItaly