Göran Kauermann

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Due to the increasing availability of spatial or spatio-temporal regression data, models that allow to incorporate the special structure of such data sets in an appropriate way are highly desired in practice. A flexible modeling approach should not only be able to account for spatial and temporal correlations, but also to model further covariate effects in(More)
The paper discusses penalised spline (P -spline) smoothing for hazard regression of multivariable survival data. Non-proportional hazard functions are fitted in a numerically handy manner by employing Poisson regression which results from numerical integration of the cumulative hazard function. Multivariate smoothing parameters are selected by utilizing the(More)
This paper proposes a numerically simple routine for locally adaptive smoothing. The locally heterogeneous regression function is modelled as a penalized spline with a smoothly varying smoothing parameter modelled as another penalized spline. This is being formulated as hierarchical mixed model, with spline coefficients following a normal distribution,(More)
plications in Genetics and Molecular Biology 4: Article 32. Juliane Schäfer und Korbinian Strimmer. 2005. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21:754–764. Juliane Schäfer und Korbinian Strimmer. 2005. Learning large-scale graphical Gaussian models from genomic data. Summary The present work is(More)
The paper discusses asymptotic properties of penalized spline smoothing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non-normal responses. The results are extended in two ways. First, assuming the spline coefficients to be a priori normally distributed links the smoothing framework(More)
Multi-phase surveys are often conducted in forestry, with the goal of estimating tree characteristics and volume over large regions. Design-based estimation of such quantities, based on information gathered during ground visits of sampled plots, can be made more precise by incorporating auxiliary information available from remote sensing. The exact(More)
A procedure is derived for computing standard errors of EM estimates in generalized linear models with random effects. Quadrature formulas are used to approximate the integrals in the EM algorithm, where two different approaches are pursued, i.e., Gauss-Hermite quadrature in the case of Gaussian random effects and nonparametric maximum likelihood estimation(More)