Skip to main content
placeholder image

Maximum likelihood estimation of covariance parameters in the spatial-random effects model

Conference Paper


Abstract


  • With the rapid increase in the size of geostatistical data sets, scalable methods for spatial data analysis, such as the spatial-random-effects model (Fixed Rank Kriging), are becoming increasingly important. We explore maximum likelihood estimation of the variance and covariance parameters in this model, and we propose the use of an E-M algorithm for this purpose. Some properties of the E-M estimators are discussed. We also compare their performance to that of the existing binned method-of-moments estimators on simulated data and on a global data set of model-based CO2.

UOW Authors


Publication Date


  • 2009

Citation


  • Katzfuss, M. & Cressie, N. A. (2009). Maximum likelihood estimation of covariance parameters in the spatial-random effects model. Proceedings of the Joint Statistical Meetings (p. 1).

Start Page


  • 1

Abstract


  • With the rapid increase in the size of geostatistical data sets, scalable methods for spatial data analysis, such as the spatial-random-effects model (Fixed Rank Kriging), are becoming increasingly important. We explore maximum likelihood estimation of the variance and covariance parameters in this model, and we propose the use of an E-M algorithm for this purpose. Some properties of the E-M estimators are discussed. We also compare their performance to that of the existing binned method-of-moments estimators on simulated data and on a global data set of model-based CO2.

UOW Authors


Publication Date


  • 2009

Citation


  • Katzfuss, M. & Cressie, N. A. (2009). Maximum likelihood estimation of covariance parameters in the spatial-random effects model. Proceedings of the Joint Statistical Meetings (p. 1).

Start Page


  • 1