Göran Kauermann

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This article proposes a new small area estimation approach that combines small area random effects with a smooth, nonparametrically specified trend. By using penalized splines as the representation for the nonparametric trend, it is possible to express the small area estimation problem as a mixed effect model regression. This model is readily fitted using(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 q u a n tities, 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)
The paper introduces a new method for flexible spline fitting for copula density estimation. Spline coefficients are penalized to achieve a smooth fit. To weaken the curse of dimensionality, instead of a full tensor spline basis, a reduced tensor product based on sparse grids Zenger (1991) is used. To achieve uniform margins of the copula density, linear(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)
This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. The implications of these ndings are discussed for local scoring estimators, a widely used class of estimators for generalized additive models described in Hastie and Tibshirani (1990).
This article presents a modified Newton method for minimizing multidi-mensional bandwidth selection for estimation in generalized additive models. The method is based on the Generalized Cross-Validation criterion applied to backfitting estimates. The approach in particular is applicable to higher dimensional problems and provides a computationally efficient(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. Multi-variate smoothing parameters are selected by utilizing the(More)