G
Göran Kauermann
Researcher at Ludwig Maximilian University of Munich
Publications - 183
Citations - 3170
Göran Kauermann is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: Smoothing & Computer science. The author has an hindex of 26, co-authored 155 publications receiving 2752 citations. Previous affiliations of Göran Kauermann include Bielefeld University & German Institute for Economic Research.
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A Note on the Efficiency of Sandwich Covariance Matrix Estimation
TL;DR: In this article, the authors investigate the consistency of the sandwich estimator in quasi-likelihood models asymptotically and analytically, and show that under certain circumstances when the quasilikelihood model is correct, the sandwich estimate is often far more variable than the usual parametric variance estimate.
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Some asymptotic results on generalized penalized spline smoothing
TL;DR: In this paper, the authors discuss asymptotic properties of penalized spline smoothing if the spline basis increases with the sample size and show that the posterior distribution of spline coefficients is approximately normal.
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A Note on Penalized Spline Smoothing With Correlated Errors
TL;DR: It is shown that a maximum likelihood–based choice of the smoothing parameter is more robust and that for a moderately misspecified correlation structure over- or (under-)fitting does not occur.
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Fast adaptive penalized splines
TL;DR: In this paper, the Laplace approximation of the marginal likelihood is used for locally adaptive smoothing, and the idea is extended to spatial and non-normal response smoothing by using a hierarchical mixed model with spline coefficients following zero mean normal distribution with a smooth variance structure.
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Expectile and quantile regression—David and Goliath?
TL;DR: In this article, the authors propose an alternative approach to extend available regression models by describing more general properties of the response distribution, such as the response probability distribution of a regression model.