10,844 research outputs found
NASA ground communications
As part of the Communications Requirements and Constraints, NASA's two major Ground Data Networks were briefly described. The NASA Communication Network, called NASCOM, is the worldwide operational telecommunications system which interconnects as the tracking and telemetry acquisition sites, launch areas, mission and project control centers, data capture facilities, and network control centers in support of space flight. For the Space Station era, NASCOM plans are set for higher data rate service utilizing data packet switched technology. Increased use of fiber optics is expected in a much more diverse network topology. The second major ground network, the Program Support Communications Network (PSCN), interconnects all NASA Centers and NASA contractor locations for intercenter non-operation communications. The primary functions are to transport voice, video, data and facsimile information for intercenter coordination, and to provide user access to space science and applications data bases. For the Space Station era, PSCN plans address the significant increase in forecast requirements for science data distribution and access to the Numerical Aerodynamics Simulator, and increased use of the Video Teleconference System
High-dimensional variable selection
This paper explores the following question: what kind of statistical
guarantees can be given when doing variable selection in high-dimensional
models? In particular, we look at the error rates and power of some multi-stage
regression methods. In the first stage we fit a set of candidate models. In the
second stage we select one model by cross-validation. In the third stage we use
hypothesis testing to eliminate some variables. We refer to the first two
stages as "screening" and the last stage as "cleaning." We consider three
screening methods: the lasso, marginal regression, and forward stepwise
regression. Our method gives consistent variable selection under certain
conditions.Comment: Published in at http://dx.doi.org/10.1214/08-AOS646 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Genome-Wide Significance Levels and Weighted Hypothesis Testing
Genetic investigations often involve the testing of vast numbers of related
hypotheses simultaneously. To control the overall error rate, a substantial
penalty is required, making it difficult to detect signals of moderate
strength. To improve the power in this setting, a number of authors have
considered using weighted -values, with the motivation often based upon the
scientific plausibility of the hypotheses. We review this literature, derive
optimal weights and show that the power is remarkably robust to
misspecification of these weights. We consider two methods for choosing weights
in practice. The first, external weighting, is based on prior information. The
second, estimated weighting, uses the data to choose weights.Comment: Published in at http://dx.doi.org/10.1214/09-STS289 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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