63,621 research outputs found
Lambert W random variables - a new family of generalized skewed distributions with applications to risk estimation
Originating from a system theory and an input/output point of view, I
introduce a new class of generalized distributions. A parametric nonlinear
transformation converts a random variable into a so-called Lambert
random variable , which allows a very flexible approach to model skewed
data. Its shape depends on the shape of and a skewness parameter .
In particular, for symmetric and nonzero the output is skewed.
Its distribution and density function are particular variants of their input
counterparts. Maximum likelihood and method of moments estimators are
presented, and simulations show that in the symmetric case additional
estimation of does not affect the quality of other parameter
estimates. Applications in finance and biomedicine show the relevance of this
class of distributions, which is particularly useful for slightly skewed data.
A practical by-result of the Lambert framework: data can be "unskewed." The
package http://cran.r-project.org/web/packages/LambertWLambertW developed
by the author is publicly available (http://cran.r-project.orgCRAN).Comment: Published in at http://dx.doi.org/10.1214/11-AOAS457 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
The Hyper Suprime-Cam Software Pipeline
In this paper, we describe the optical imaging data processing pipeline
developed for the Subaru Telescope's Hyper Suprime-Cam (HSC) instrument. The
HSC Pipeline builds on the prototype pipeline being developed by the Large
Synoptic Survey Telescope's Data Management system, adding customizations for
HSC, large-scale processing capabilities, and novel algorithms that have since
been reincorporated into the LSST codebase. While designed primarily to reduce
HSC Subaru Strategic Program (SSP) data, it is also the recommended pipeline
for reducing general-observer HSC data. The HSC pipeline includes high level
processing steps that generate coadded images and science-ready catalogs as
well as low-level detrending and image characterizations.Comment: 39 pages, 21 figures, 2 tables. Submitted to Publications of the
Astronomical Society of Japa
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