We develop nonparametric tests for the null hypothesis that a function has a prescribed form, to apply to data sets with missing observations. Omnibus nonparametric tests do not need to specify a particular alternative parametric form, and have power against a large range of alternatives, the...

In this paper the interest is in regression analysis for data that show possibly overdispersion or underdispersion. The starting point for modeling are generalized linear models in which we no longer admit a linear form for the mean regression function, but allow it to be any smooth function of...

In this paper we obtain the asymptotic distribution of restricted likelihood ratio tests in mixed linear models with a fixed and finite number of random effects. We explain why for such models the often quoted 50:50 mixture of a chi-s quared random variable with one degree of freedom and a point...

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...

Application of classical model selection methods such as Akaike's information criterion AIC becomes problematic when observations are missing. In this paper we propose some variations on the AIC, which are applicable to missing covariate problems. The method is directly based on the EM algorithm...

We propose two test statistics for use in inverse regression problems Y = Kcedil; + , where K is a given matrix or operator which cannot be continuously inverted. Thus, only noisy, indirect observations Y for the function cedil; are available. The tests are designed for hypotheses of the form H0...

Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on penalized spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights calibrated to known control totals....

In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the Focussed Information Criterion (FIC) for variable selection in logistic regression. The FIC gives, depending on the quantity to be estimated, possibly different...

We propose 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...

Recently, Hjort and Claeskens (2003) developed an asymptotic theory for model selection, model averaging and post-model selection/averaging inference using likelihood methods in parametric models, along with associated confidence statements. In this paper, we consider a semiparametric version of...