Sampling from Linear Multivariate Densities

It is well known that the generation of random vectors with non-independent components is difficult. Nevertheless, we propose a new and very simple generation algorithm for multivariate linear densities over point-symmetric domains. Among other applications it can be used to design a simple decomposition-rejection algorithm for multivariate concave distributions.Series: Research Report Series / Department of Statistics and Mathematic

lassopack: Model selection and prediction with regularized regression in Stata

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, panel and time-series data (cvlasso), and theory-driven (`rigorous') penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.Comment: 52 pages, 6 figures, 6 tables; submitted to Stata Journal; for more information see https://statalasso.github.io

Catastrophic disruptions revisited

We use a smooth particle hydrodynamics method (SPH) to simulate colliding rocky and icy bodies from cm-scale to hundreds of km in diameter, in an effort to define self-consistently the threshold for catastrophic disruption. Unlike previous efforts, this analysis incorporates the combined effects of material strength (using a brittle fragmentation model) and self-gravitation, thereby providing results in the ``strength regime'' and the ``gravity regime'', and in between. In each case, the structural properties of the largest remnant are examined.Comment: To appear in Icaru

Muon Track Reconstruction and Data Selection Techniques in AMANDA

The Antarctic Muon And Neutrino Detector Array (AMANDA) is a high-energy neutrino telescope operating at the geographic South Pole. It is a lattice of photo-multiplier tubes buried deep in the polar ice between 1500m and 2000m. The primary goal of this detector is to discover astrophysical sources of high energy neutrinos. A high-energy muon neutrino coming through the earth from the Northern Hemisphere can be identified by the secondary muon moving upward through the detector. The muon tracks are reconstructed with a maximum likelihood method. It models the arrival times and amplitudes of Cherenkov photons registered by the photo-multipliers. This paper describes the different methods of reconstruction, which have been successfully implemented within AMANDA. Strategies for optimizing the reconstruction performance and rejecting background are presented. For a typical analysis procedure the direction of tracks are reconstructed with about 2 degree accuracy.Comment: 40 pages, 16 Postscript figures, uses elsart.st