The Earth's Gamma-ray Albedo as observed by EGRET

The Earth's high energy gamma-ray emission is caused by cosmic ray interactions with the atmosphere. The EGRET detector on-board the CGRO satellite is only the second experiment (after SAS-2) to provide a suitable dataset for the comprehensive study of this emission. Approximately 60% of the EGRET dataset consist of gamma photons from the Earth. This conference contribution presents the first results from the first analysis project to tackle this large dataset. Ultimate purpose is to develop an analytical model of the Earth's emission for use in the GLAST project. The results obtained so far confirm the earlier results from SAS-2 and extend them in terms of statistical precision and angular resolution.Comment: To be published in the proceedings of the Gamma 2004 Symposium on High-Energy Gamma-Ray Astronomy, Heidelberg, July, 2004 (AIP Proceedings Series

Faster Background Determination - a method for gaining time coverage and flux measurement accuracy with Cherenkov telescopes

An improved way of taking off-source data for background determination in Cherenkov telescope observations is proposed. Generalizing the traditional concept of taking on-source/off-source observations of equal duration (e.g. 30 minutes ON followed by 30 minutes OFF), Faster Background Determination (FBD) permits an off-source observation with the same zenith angle distribution as the on-source observation to be obtained within less time. The method permits the on-source observation time to be maximized without compromising the quality of the background determination. It also increases the signal significance for strong sources. The only modification necessary in the data acquisition is a small change to the tracking algorithm. The only modification necessary in the data analysis is to introduce a time normalization which does not increase the systematic errors. The method could become the normal observing mode for Cherenkov telescopes when observing strong sources.Comment: LaTeX, 13 pages, 4 figures, Astropart. Phys., in pres

Nonparametric Estimation of the Link Function Including Variable Selection

Nonparametric methods for the estimation of the link function in generalized linear models are able to avoid bias in the regression parameters. But for the estimation of the link typically the full model, which includes all predictors, has been used. When the number of predictors is large these methods fail since the full model can not be estimated. In the present article a boosting type method is proposed that simultaneously selects predictors and estimates the link function. The method performs quite well in simulations and real data examples

Shrinkage and Variable Selection by Polytopes

Constrained estimators that enforce variable selection and grouping of highly correlated data have been shown to be successful in finding sparse representations and obtaining good performance in prediction. We consider polytopes as a general class of compact and convex constraint regions. Well established procedures like LASSO (Tibshirani, 1996) or OSCAR (Bondell and Reich, 2008) are shown to be based on specific subclasses of polytopes. The general framework of polytopes can be used to investigate the geometric structure that underlies these procedures. Moreover, we propose a specifically designed class of polytopes that enforces variable selection and grouping. Simulation studies and an application illustrate the usefulness of the proposed method