Gerda Claeskens

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Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA School of Oper. Res. and Ind. Eng., Cornell University, Rhodes Hall, NY 14853, USA Institute of Statistics, Université Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium Department of Statistics, School of Mathematics, University of New South Wales,(More)
Using support vector machines for classification problems has the advantage that the curse of dimensionality is circumvented. However, it has been shown that even here a reduction of the dimension of the input space leads to better results. For this purpose, we propose two information criteria which can be computed directly from the definition of the(More)
Standard variable selection procedures, primarily developed for the construction of outcome prediction models, are routinely applied when assessing exposure effects in observational studies. We argue that this tradition is sub-optimal and prone to yield bias in exposure effect estimators as well as their corresponding uncertainty estimators. We weigh the(More)
In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the focused information criterion (FIC) for variable selection in logistic regression. The FIC gives, depending on the quantity to be estimated, possibly different sets of selected variables. The standard version of the FIC(More)
In this paper we construct simultaneous confidence bands for a smooth curve using penalized spline estimators. We consider three types of estimation methods: (i) as a standard (fixed effect) nonparametric model, (ii) using the mixed model framework with the spline coefficients as random effects and (iii) a Bayesian approach. The volume-of-tube formula is(More)
Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions about both the distribution of the data and about underlying regression relationships. If such parametric assumptions do not hold, the multiply imputed data are not appropriate and might produce inconsistent estimators and thus misleading(More)
When the data do not come from the assumed parametric model, the usual asymptotic chisquared distribution under the null hypothesis, remains valid for “robustified” Wald and score test statistics. In this paper we compare the performance of this chi-squared approximation to that of a semiparametric bootstrap method. The bootstrap approximation is based on a(More)
In order to make predictions of future values of a time series, one needs to specify a forecasting model. A popular choice is an autoregressive time series model, where the order of the model is chosen by an information criterion. We propose an extension of the Focussed Information Criterion (FIC) for model-order selection with focus on a high predictive(More)