Asymptotic Confidence Regions Based on the Adaptive Lasso with Partial Consistent Tuning

We construct confidence sets based on an adaptive Lasso estimator with componentwise tuning in the framework of a low-dimensional linear regression model. We consider the case where at least one of the components is penalized at the rate of consistent model selection and where certain components may not be penalized at all. We perform a detailed study of the consistency properties and the asymptotic distribution that includes the effects of componentwise tuning within a so-called moving-parameter framework. These results enable us to explicitly provide a set $\mathcal{M}$ such that every open superset acts as a confidence set with uniform asymptotic coverage equal to 1 whereas every proper closed subset with non-empty interior is a confidence set with uniform asymptotic coverage equal to 0. The shape of the set $\mathcal{M}$ depends on the regressor matrix as well as the deviations within the componentwise tuning parameters. Our findings can be viewed as a generalization of P\"otscher & Schneider (2010) who considered confidence intervals based on components of the adaptive Lasso estimator for the case of orthogonal regressors

Confidence Sets Based on Penalized Maximum Likelihood Estimators in Gaussian Regression

Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-sample coverage properties of such intervals are determined and it is shown that symmetric intervals are the shortest. The length of the shortest intervals based on the hard-thresholding estimator is larger than the length of the shortest interval based on the adaptive LASSO, which is larger than the length of the shortest interval based on the LASSO, which in turn is larger than the standard interval based on the maximum likelihood estimator. In the case where the penalized estimators are tuned to possess the `sparsity property', the intervals based on these estimators are larger than the standard interval by an order of magnitude. Furthermore, a simple asymptotic confidence interval construction in the `sparse' case, that also applies to the smoothly clipped absolute deviation estimator, is discussed. The results for the known-variance case are shown to carry over to the unknown-variance case in an appropriate asymptotic sense.Comment: second revision: new title, some comments added, proofs moved to appendi

The Scarring Effect of the 2008 Economic Crisis: Growth and Growth Decline in Austria’s Nonprofit Social Services Sector

Since the 2008 economic crisis, social service providers worldwide have reported funding cuts, while the need for some social services has been increasing. This paper examines the combined and longer-term effects of such divergent developments on the nonprofit social services sector. The empirical analysis uses Austrian administrative data on six subfields of the sector covering the years 2003–2017. We investigate significant changes in the trends of four growth indicators applying interrupted time series analysis. We find that the 2008 economic crisis is associated with persistently lower growth rates in Austria’s nonprofit social services sector. The magnitude of this dampening effect differs across subsectors. Additionally, our findings suggest an increase in market concentration. Hence, the study discloses a long-term scarring effect of the economic crisis on Austria’s social services sector, raising doubts on the sector’s future resilience

„Unbewältigte Vergangenheit“? : Faschismus und Krieg in Literatur und Film um 1960 ; 23.11.2007 – 25.11.2007 Potsdam

Tagungsbericht gehalten auf der Tagung des Instituts für Germanistik der Universität Potsdam in Zusammenarbeit mit dem Zentrum für Zeithistorische Forschung am 23.11.2007-25.11.2007 in Potsda

The modular structure of an ontology: Atomic decomposition

Extracting a subset of a given ontology that captures all the ontology’s knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules. However, a single module does not allow us to understand neither topicality, connectedness, structure, or superfluous parts of an ontology, nor agreement between actual and intended modeling. The strong logical properties of locality-based modules suggest that the family of all such modules of an ontology can support comprehension of the ontology as a whole. However, extracting that family is not feasible, since the number of localitybased modules of an ontology can be exponential w.r.t. its size. In this paper we report on a new approach that enables us to efficiently extract a polynomial representation of the family of all locality-based modules of an ontology. We also describe the fundamental algorithm to pursue this task, and report on experiments carried out and results obtained.

Nonprofit Organisationen in Österreich 2006

Series: Forschungsberichte / Institut für Sozialpoliti