SENSITIVITY ANALYSIS IN SPATIAL STATISTICS : DETECTING INFLUENTIAL OBSERVATIONS IN SPATIAL PREDICTION

• CHOI SeungBae

  Graduate School of Natural Science and Technology, Osaka University

• TANAKA Yutaka

  Faculty of Environmental and Mathematical Sciences, Okayama University

Abstract

An important problem in spatial statistics is to predict the unobserved value z(80) at a specified location s_o based on the information of n observations z(8α), α=1,・・・, n. It can be achieved in three stages of (1) estimating the variograms, (2) fitting a model to the estimated variograms, and (3) applying the so-called ordinary (or universal) kriging. The present article proposes a method to detect influential observations in variogram estimation, variogram model fitting to the estimated variograms, and spatial prediction using the fitted variogram model. To do this, we derive the influence functions for statistics in the above three stages assuming that the underlying process of the observed spatial data is second-order stationary. A real numerical example is analyzed to show the validity or usefulness of the proposed influence functions. Comparison is made with the influence function derived by Gunst and Hartfield (1997).

Journal

• Journal of the Japanese Society of Computational Statistics

  Journal of the Japanese Society of Computational Statistics 13 (1), 25-39, 2000-12-01

  Japanese Society of Computational Statistics

Keywords

• Influence function
• Sample variogram
• Robust variogram
• Variogram-model fitting
• Ordinary kriging

Details

• CRID: 1571698601860769408
• NII Article ID: 110001235635
• NII Book ID: AA10823693
• ISSN: 09152350
• Text Lang: en
• Data Source:
  • CiNii Articles