Article thumbnail

Spatial Sampling Design Based on Stochastic Complexity

By M. C. Bueso, J. M. Angulo, G. Qian and F. J. Alonso

Abstract

A new methodology is introduced for spatial sampling design when the variable of interest cannot be directly observed, but information on it can be obtained by sampling a related variable, and estimation of the underlying model is required. An approach based on entropy has been proposed by Bueso, Angulo, and Alonso (1998, Environ. Ecol. Statist.5, No. 1, 29-44) in the case where a model for the involved variables is given. However, in some cases a predetermined structure modelling the behaviour of the variables cannot be assumed. In this context, we derive criteria for solving the design problem based on the stochastic complexity theory and on the philosophy of the EM algorithm. For applying the proposed criteria a computational procedure is developed based on the supplemented EM algorithms. The methodology is illustrated with a numerical example.EM algorithms incomplete data minimum description length network design stochastic complexity

OAI identifier:
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.sciencedirect.com/s... (external link)

  • To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

    Suggested articles