Frailty models account for the clustering present in grouped event time data. A proportional hazards model with shared frailties expresses the hazard for each subject. Often a one-parameter gamma distribution is assumed for the frailties. In this paper we construct formal goodness-of-fit tests...

Frailty models account for the clustering present in grouped event time data. A proportional hazards model with shared frailties expresses the hazard for each subject. Often a one-parameter gamma distribution is assumed for the frailties. In this paper we construct formal goodness-of-fit tests...

We present a focused selection method for social networks. The procedure is driven by a focus, the main quantity we want to estimate well. It represents the statistical translation of a research hypothesis into parameters of interest. Given a collection of models, the procedure estimates for...

Variable selection methods and model selection approaches are valuable statistical tools, which are indispensable for almost any statistical modeling question. This review considers first the use of information criteria for model selection. Such criteria provide an ordering of the considered...

Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM...

The focused information criterion for model selection is constructed to select the model that best estimates a particular quantity of interest, the focus, in terms of mean squared error. We extend this focused selection process to the high-dimensional regression setting with potentially a larger...

In this addendum to Verbelen et al. (2015), we present several additional examples of the calibration procedure for fitting multivariate mixtures of Erlangs to censored and truncated data.The paper "Multivariate Mixtures of Erlangs for Density Estimation Under Censoring and Truncation" to which...

Ignoring the model selection step in inference after selection is harmful. This paper studies the asymptotic distribution of estimators after model selection using the Akaike information criterion. First, we consider the classical setting in which a true model exists and is included in the...

Variable selection in the presence of outliers may be performed by using a robust version of Akaike's information criterion AIC. In this paper explicit expressions are obtained for such criteria when S and MM-estimators are used. The performance of these criteria is compared to the existing AIC...

Phase variation in functional data obscures the true amplitude variation when a typical cross-sectional analysis of these responses would be performed. Time warping or curve registration aims at eliminating the phase variation, typically by applying a transformation, the warping function 'pi' ,...