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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)
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)
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)
The paper considers smooth modelling of hazard functions, where dynamics is modelled in both, duration time and calendar time. The model is specified with time dynamic covariate effects to replace restrictive assumptions of proportional hazards. Additivity of the time effects is assumed which allows for simple estimation in a backfitting style. Penalized(More)
We describe and contrast several different bootstrapping procedures for penalized spline smoothers. The bootstrapping procedures considered are variations on existing methods, developed under two different probabilistic frameworks. Under the first framework, penalized spline regression is considered an estimation technique to find an unknown smooth(More)