Advanced Statistical Methods For Improved Data Analysis Of Nasa Astrophysics Missions

The investigators under this grant studied ways to improve the statistical analysis of astronomical data. They looked at existing techniques, the development of new techniques, and the production and distribution of specialized software to the astronomical community. Abstracts of nine papers that were produced are included, as well as brief descriptions of four software packages. The articles that are abstracted discuss analytical and Monte Carlo comparisons of six different linear least squares fits, a (second) paper on linear regression in astronomy, two reviews of public domain software for the astronomer, subsample and half-sample methods for estimating sampling distributions, a nonparametric estimation of survival functions under dependent competing risks, censoring in astronomical data due to nondetections, an astronomy survival analysis computer package called ASURV, and improving the statistical methodology of astronomical data analysis. Feigelson, Eric D. Unspecified Center NASA-CR-193514, NAS 1.26:193514 NAGW-1917...

Effective science, clearly a mandate for the National Aeronautics and Space Administration (NASA), involves asking significant questions about the physical and biological world and seeking definitive answers. Its product is new knowledge that has value to the nation. NASA's flight projects are highly visible and usually the most costly elements of this process, but they are only a part of the science enterprise. Flight projects are founded on research that defines clear scientific goals and questions, designs missions to address those questions, and develops the required technologies to accomplish the missions. This research is funded primarily by NASA's research and analysis (R&A) programs. Data from flight projects are transformed into knowledge through analysis and synthesis-research that is funded both by R&A and by the data analysis (DA) portion of mission operations and data analysis (MO&DA) programs. R&A and DA programs are the subject of this report and are grouped for convenience under the heading of research and data analysis (R&DA).

A directory of U.S. government statistics publications by issuing agency. Entries include GPO stock number, LC and Dewey classification, OCLC and ISSN numbers, and sometimes a description. Includes geographic index.