Technical Report // Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2003,18
We examine the hypothesis of an increase of humus disintegration by analyzing chemical substances measured in the seepage water of a German forest. Problems arise because of a large percentage of missing observations. We use a regression model with spatial and temporal effects constructed in an exploratory data analysis. Spatial dependencies are modelled by random effects and an autoregressive structure for observations in distinct soil depths resulting in a recursive linear mixed model structure. Temporal dependencies are included by an autoregressive structure of the random effects. For parameter estimation an EM algorithm is deduced assuming the errors to be Gaussian. As a result of the data analysis we specify chemical substances which possibly affect the process of humus disintegration. In particular, we find evidence that the presence of aluminium ions is important, but because of the high correlations among the regressors this might be due to confounding with iron.
Autoregressive model Maximum likelihood estimation Missing data Mixed effects Recursive linear model Spatial-temporal correlations