Nonstationary conditional models for spatial data based on varying coefficients

Dreesman, J. and Tutz, Gerhard (1999): Nonstationary conditional models for spatial data based on varying coefficients. Collaborative Research Center 386, Discussion Paper 150

Abstract

The analysis of spatial data by means of Markov random fields usually is based on strict stationarity assumptions. Although these assumptions rarely hold, they are necessary in order to obtain parameter estimates. For Gaussian data the necessary assumptions are mean- and covariance stationarity. While simple techniques are available to deal with violations of mean stationarity, the same is not true for covariance stationarity. In order to handle mean nonstationarity as well as covariance nonstationarity, we propose the modelling by spatially varying coefficients. This aproach not only yields more appropriate models for nonstationary data but also can be used to detect violations of the stationarity assumptions. The method is illustrated by use of the well known wheat yield data.

• Item Type: Paper
• Keywords: Markov Random Fields, Local Likelihood, Pseudolikelihood, Wheat Yield Data
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English